commit 9f3dfdace9f415c83d5541ec1792378eb42c92ae Author: Hamza Zakaria Date: Tue May 12 10:16:56 2026 +0100 First Commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8f42b9e --- /dev/null +++ b/.gitignore @@ -0,0 +1,234 @@ +# Auto Label +.autolabel_staging/ +_rejected_/ +_unreviewed_/ + +# Crops Dataset +crops_dataset/ +crops_dataset.zip + +# Archived files. +*.zip +*.7z +*.rar + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +*.lcov +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +# Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +# poetry.lock +# poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +# pdm.lock +# pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +# pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi/* +!.pixi/config.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule* +celerybeat.pid + +# Redis +*.rdb +*.aof +*.pid + +# RabbitMQ +mnesia/ +rabbitmq/ +rabbitmq-data/ + +# ActiveMQ +activemq-data/ + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +# .idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ +# Temporary file for partial code execution +tempCodeRunnerFile.py + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml diff --git a/1_generate_crop_dataset.py b/1_generate_crop_dataset.py new file mode 100644 index 0000000..d7d47d4 --- /dev/null +++ b/1_generate_crop_dataset.py @@ -0,0 +1,194 @@ +""" +Script 1 — Crop Dataset Generator +=================================== +Runs your fine-tuned YOLOv8n model on a folder of shelf images, +crops every detection, and dumps everything into a single flat folder. + +You classify ONCE, then run script 1b_split_dataset.py to create +the train / val / test split automatically. + +Output structure +---------------- +data/ + unknown/ + img001_crop0000.jpg + img001_crop0001.jpg + img002_crop0000.jpg + ... + +After generation +---------------- +1. Rename / move crops into class subfolders inside data/: + data/ + cola_can/ img001_crop0000.jpg … + pepsi_can/ img003_crop0002.jpg … + … + + Use Label Studio, a file manager, or any annotation tool. + The `unknown/` folder holds anything you haven't labelled yet. + +2. Then run: + python 1b_split_dataset.py --data_dir data --output_dir crops_dataset + +Usage +----- +python 1_generate_crop_dataset.py \ + --source /path/to/shelf/images \ + --weights /path/to/best.pt \ + --output_dir data \ + --conf 0.25 \ + --iou 0.45 \ + --img_size 640 \ + --padding 10 \ + --min_size 32 +""" + +import argparse +from pathlib import Path + +import cv2 +import numpy as np +from ultralytics import YOLO +from tqdm import tqdm + + +# ─────────────────────────── helpers ───────────────────────────────────────── + +def parse_args(): + p = argparse.ArgumentParser(description="Generate flat crop dataset from YOLOv8 detections") + p.add_argument("--source", required=True , help="Directory with shelf images (jpg/png/…)") + p.add_argument("--weights", default="detector/best.pt", help="Path to your fine-tuned YOLOv8n .pt file") + p.add_argument("--output_dir", default="data" , help="Root output dir — crops go into /unknown/") + p.add_argument("--conf", type=float, default=0.25 , help="Detection confidence threshold") + p.add_argument("--iou", type=float, default=0.45 , help="NMS IoU threshold") + p.add_argument("--img_size", type=int, default=640 , help="YOLO inference image size") + p.add_argument("--padding", type=int, default=10 , help="Extra pixels around each crop (px)") + p.add_argument("--min_size", type=int, default=32 , help="Discard crops smaller than this (px)") + p.add_argument("--save_labels", action="store_true", + help="Save a sidecar .txt with YOLO cls_id / confidence next to each crop") + return p.parse_args() + + +def get_image_paths(source: str): + exts = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"} + p = Path(source) + + if p.is_file(): + # Single image passed directly e.g. --source 01.jpg + if p.suffix.lower() not in exts: + raise ValueError(f"File is not a supported image type: {p}") + return [p] + + if p.is_dir(): + # Folder — recurse for all images + paths = sorted(f for f in p.rglob("*") if f.suffix.lower() in exts) + if not paths: + raise FileNotFoundError(f"No images found in directory: {p}") + return paths + + # Glob pattern e.g. --source "frames/*.jpg" + paths = sorted(f for f in Path(".").glob(source) if f.suffix.lower() in exts) + if not paths: + raise FileNotFoundError(f"No images matched: {source}") + return paths + + +def crop_detection(image: np.ndarray, box, padding: int, min_size: int): + """ + Extract one padded crop from *image* using *box* (xyxy). + Returns the crop array, or None if it is too small. + """ + x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) + h, w = image.shape[:2] + + x1 = max(0, x1 - padding) + y1 = max(0, y1 - padding) + x2 = min(w, x2 + padding) + y2 = min(h, y2 + padding) + + if (x2 - x1) < min_size or (y2 - y1) < min_size: + return None + + return image[y1:y2, x1:x2] + + +# ─────────────────────────── main ──────────────────────────────────────────── + +def main(): + args = parse_args() + out_dir = Path(args.output_dir) / "unknown" + out_dir.mkdir(parents=True, exist_ok=True) + + # ── Load model ──────────────────────────────────────────────────────────── + print(f"[1/2] Loading model: {args.weights}") + model = YOLO(args.weights) + + # ── Detect & crop ───────────────────────────────────────────────────────── + image_paths = get_image_paths(args.source) + print(f"[2/2] Found {len(image_paths)} images — running detection …\n") + + total_saved = 0 + total_skip = 0 + + for img_path in tqdm(image_paths, desc="Detecting & cropping"): + image = cv2.imread(str(img_path)) + if image is None: + print(f" ⚠ Cannot read {img_path.name} — skipping") + continue + + results = model( + image, + conf=args.conf, + iou=args.iou, + imgsz=args.img_size, + verbose=False, + )[0] + + stem = img_path.stem + for i, box in enumerate(results.boxes): + crop = crop_detection(image, box, args.padding, args.min_size) + + if crop is None: + total_skip += 1 + continue + + crop_name = f"{stem}_crop{i:04d}.jpg" + crop_path = out_dir / crop_name + cv2.imwrite(str(crop_path), crop) + total_saved += 1 + + if args.save_labels: + cls_id = int(box.cls[0]) + conf = float(box.conf[0]) + cls_name = model.names.get(cls_id, str(cls_id)) + crop_path.with_suffix(".txt").write_text( + f"{cls_id} {cls_name} {conf:.4f}\n" + ) + + # ── Summary ─────────────────────────────────────────────────────────────── + print("\n" + "=" * 55) + print(" Crop generation complete!") + print(f" Saved : {total_saved:,} crops -> {out_dir.resolve()}") + print(f" Skipped : {total_skip:,} (below --min_size={args.min_size}px)") + print("=" * 55) + print(""" + Next steps: + 1. Organise crops into class subfolders inside data/: + data/ + cola_can/ *.jpg + pepsi_can/ *.jpg + ... + Leave unlabelled crops in data/unknown/ — the split + script ignores that folder automatically. + + 2. Run the splitter: + python 1b_split_dataset.py \\ + --data_dir data \\ + --output_dir crops_dataset \\ + --val_split 0.15 \\ + --test_split 0.05 + """) + + +if __name__ == "__main__": + main() diff --git a/1b_split_dataset.py b/1b_split_dataset.py new file mode 100644 index 0000000..5115eca --- /dev/null +++ b/1b_split_dataset.py @@ -0,0 +1,221 @@ +""" +Script 1b — Dataset Splitter +============================== +Takes your labelled data/ folder (one subfolder per class) and splits +it into the train / val / test structure expected by the training script. + +The split is STRATIFIED — every class gets the same train/val/test ratio, +so rare classes are not accidentally wiped out of val or test. + +Input layout (after you've labelled crops from script 1) +--------------------------------------------------------- +data/ + cola_can/ ← labelled class folders + img001_crop0000.jpg + img002_crop0003.jpg + ... + pepsi_can/ + ... + unknown/ ← IGNORED automatically (unlabelled crops) + ... + +Output layout +------------- +crops_dataset/ + train/ + cola_can/ ... + pepsi_can/ ... + val/ + cola_can/ ... + pepsi_can/ ... + test/ (only if --test_split > 0) + cola_can/ ... + pepsi_can/ ... + +Files are COPIED by default (--move to use move instead). +Original data/ folder is left intact unless --move is used. + +Usage +----- +python 1b_split_dataset.py \ + --data_dir data \ + --output_dir crops_dataset \ + --val_split 0.15 \ + --test_split 0.05 \ + --seed 42 + +Then run: + python 2_train_classifier.py --data_dir crops_dataset +""" + +import argparse +import json +import random +import shutil +from collections import defaultdict +from pathlib import Path + + +# ─────────────────────────── args ──────────────────────────────────────────── + +def parse_args(): + p = argparse.ArgumentParser(description="Stratified train/val/test splitter") + p.add_argument("--data_dir", required=True, help="Labelled data root (subfolders = classes)") + p.add_argument("--output_dir", default="crops_dataset", help="Where to write the split dataset") + p.add_argument("--val_split", type=float, default=0.15, help="Fraction for validation (default 0.15)") + p.add_argument("--test_split", type=float, default=0.05, help="Fraction for test — 0 to skip") + p.add_argument("--seed", type=int, default=42) + p.add_argument("--move", action="store_true", + help="Move files instead of copying (saves disk space)") + p.add_argument("--min_per_class", type=int, default=2, + help="Warn if a class has fewer than this many images after splitting") + return p.parse_args() + + +# ─────────────────────────── helpers ───────────────────────────────────────── + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"} +IGNORE_DIRS = {"unknown", "train", "val", "test"} # skip these silently + + +def collect_classes(data_dir: Path) -> dict[str, list[Path]]: + """ + Scan *data_dir* for class subfolders and return {class_name: [image_paths]}. + Folders named 'unknown', 'train', 'val', or 'test' are silently skipped. + """ + classes: dict[str, list[Path]] = {} + for folder in sorted(data_dir.iterdir()): + if not folder.is_dir(): + continue + if folder.name.lower() in IGNORE_DIRS: + print(f" ⚙ Skipping '{folder.name}/' (reserved name)") + continue + images = sorted(f for f in folder.iterdir() if f.suffix.lower() in IMAGE_EXTS) + if images: + classes[folder.name] = images + else: + print(f" ⚠ '{folder.name}/' has no images — skipped") + return classes + + +def stratified_split( + images: list[Path], + val_frac: float, + test_frac: float, + rng: random.Random, +) -> tuple[list[Path], list[Path], list[Path]]: + """ + Split *images* into (train, val, test) respecting the requested fractions. + Guarantees at least 1 image in val and test if the class is large enough. + """ + imgs = images[:] + rng.shuffle(imgs) + n = len(imgs) + + n_test = max(1, round(n * test_frac)) if test_frac > 0 else 0 + n_val = max(1, round(n * val_frac)) if val_frac > 0 else 0 + + # Ensure we don't eat into train entirely for tiny classes + n_test = min(n_test, max(0, n - 2)) + n_val = min(n_val, max(0, n - n_test - 1)) + + test = imgs[:n_test] + val = imgs[n_test : n_test + n_val] + train = imgs[n_test + n_val:] + return train, val, test + + +def transfer(src: Path, dst: Path, move: bool) -> None: + dst.parent.mkdir(parents=True, exist_ok=True) + if move: + shutil.move(str(src), str(dst)) + else: + shutil.copy2(str(src), str(dst)) + + +# ─────────────────────────── main ──────────────────────────────────────────── + +def main(): + args = parse_args() + rng = random.Random(args.seed) + + data_dir = Path(args.data_dir) + out_dir = Path(args.output_dir) + + if not data_dir.exists(): + raise FileNotFoundError(f"data_dir not found: {data_dir}") + + # ── Collect classes ─────────────────────────────────────────────────────── + print(f"\nScanning: {data_dir.resolve()}") + classes = collect_classes(data_dir) + + if not classes: + raise RuntimeError( + f"No labelled class folders found in {data_dir}.\n" + "Create subfolders named after your product classes and move crops into them." + ) + + num_classes = len(classes) + total_imgs = sum(len(v) for v in classes.values()) + print(f"\nFound {num_classes} classes | {total_imgs:,} total images\n") + + # ── Split each class separately (stratified) ────────────────────────────── + split_counts: dict[str, dict[str, int]] = defaultdict(lambda: {"train": 0, "val": 0, "test": 0}) + warnings = [] + + for cls_name, images in classes.items(): + train, val, test = stratified_split(images, args.val_split, args.test_split, rng) + + for split_name, split_imgs in [("train", train), ("val", val), ("test", test)]: + if not split_imgs: + continue + for src in split_imgs: + dst = out_dir / split_name / cls_name / src.name + transfer(src, dst, move=args.move) + split_counts[cls_name][split_name] = len(split_imgs) + + if len(train) < args.min_per_class: + warnings.append( + f" ⚠ '{cls_name}' has only {len(train)} training image(s) — " + "consider collecting more data" + ) + + # ── Write class manifest ────────────────────────────────────────────────── + class_list = sorted(classes.keys()) + class_map = {i: c for i, c in enumerate(class_list)} + manifest_path = out_dir / "class_names.json" + manifest_path.write_text(json.dumps(class_map, indent=2)) + + # ── Print summary table ─────────────────────────────────────────────────── + col_w = max(len(c) for c in classes) + 2 + + header = f" {'Class':<{col_w}} {'Train':>7} {'Val':>7} {'Test':>7} {'Total':>7}" + print("=" * len(header)) + print(header) + print("-" * len(header)) + + g_train = g_val = g_test = 0 + for cls_name in sorted(split_counts): + c = split_counts[cls_name] + tot = c["train"] + c["val"] + c["test"] + print(f" {cls_name:<{col_w}} {c['train']:>7} {c['val']:>7} {c['test']:>7} {tot:>7}") + g_train += c["train"]; g_val += c["val"]; g_test += c["test"] + + print("-" * len(header)) + print(f" {'TOTAL':<{col_w}} {g_train:>7} {g_val:>7} {g_test:>7} {g_train+g_val+g_test:>7}") + print("=" * len(header)) + + if warnings: + print() + for w in warnings: + print(w) + + action = "moved" if args.move else "copied" + print(f"\n {action.capitalize()} to : {out_dir.resolve()}") + print(f" class_names.json: {manifest_path}") + print(f"\n Next step:") + print(f" python 2_train_classifier.py --data_dir {out_dir}") + + +if __name__ == "__main__": + main() diff --git a/2_product-classifier.ipynb b/2_product-classifier.ipynb new file mode 100644 index 0000000..9d87530 --- /dev/null +++ b/2_product-classifier.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"datasetVersion","sourceId":16206274,"datasetId":10367968,"databundleVersionId":17185835}],"dockerImageVersionId":31329,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"a1b2c3d4","cell_type":"markdown","source":"# 🛒 Product Classifier — Training Notebook\n\nTrains a lightweight **EfficientNet-B0** (ImageNet pre-trained) on shelf product crops\ngenerated by `1_generate_crop_dataset.py`.\n\n---\n\n### ✅ Before you run\n1. **Runtime → Change runtime type → GPU** (T4 is free and enough for this)\n2. Upload your `crops_dataset/` as a Kaggle Dataset and attach it via **+ Add Data**\n3. Update `DATA_DIR` in **Cell 2 — Configuration** to match your dataset path\n4. Run all cells top-to-bottom\n\n### 📂 Expected dataset layout\n```\n/kaggle/input/your-dataset-name/\n train/\n cola_can/ img001.jpg …\n pepsi_can/ img002.jpg …\n lays_chips/ img003.jpg …\n val/\n cola_can/ …\n …\n```\n\n### 📦 Outputs saved to `/kaggle/working/runs/classify/`\n| File | Description |\n|---|---|\n| `best.pt` | Best checkpoint (highest val accuracy) |\n| `last.pt` | Last epoch checkpoint |\n| `class_names.json` | `{idx: class_name}` mapping for inference |\n| `training_curves.png` | Loss & accuracy plots |","metadata":{}},{"id":"b2c3d4e5","cell_type":"code","source":"# ── Cell 1 — Install / verify dependencies ────────────────────────────────────\n# Kaggle kernels ship with torch & torchvision pre-installed.\n# We only need tqdm (already present) — nothing extra to install.\n\nimport sys, subprocess\n\ndef run(cmd):\n subprocess.run(cmd, shell=True, check=True)\n\n# Verify GPU is visible\nimport torch\nprint(f\"PyTorch : {torch.__version__}\")\nprint(f\"CUDA : {torch.version.cuda}\")\nprint(f\"GPU : {torch.cuda.get_device_name(0) if torch.cuda.is_available() else '⚠ NOT available — check runtime settings'}\")\nprint(f\"VRAM : {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\" if torch.cuda.is_available() else \"\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:04.799727Z","iopub.execute_input":"2026-05-11T09:57:04.800469Z","iopub.status.idle":"2026-05-11T09:57:04.805785Z","shell.execute_reply.started":"2026-05-11T09:57:04.800441Z","shell.execute_reply":"2026-05-11T09:57:04.805146Z"}},"outputs":[{"name":"stdout","text":"PyTorch : 2.10.0+cu128\nCUDA : 12.8\nGPU : Tesla T4\nVRAM : 15.6 GB\n","output_type":"stream"}],"execution_count":16},{"id":"c3d4e5f6","cell_type":"code","source":"# ── Cell 2 — Configuration (edit this cell) ──────────────────────────────────\nfrom pathlib import Path\n\n# ── Paths ──────────────────────────────────────────────────────────────────────\n# Point this to the root of your attached dataset (the folder that contains train/ and val/)\nDATA_DIR = Path(\"/kaggle/input/datasets/triztechdata/product-dataset/crops_dataset\") # ← CHANGE THIS\nOUTPUT_DIR = Path(\"/kaggle/working/runs/classify\")\n\n# ── Model ──────────────────────────────────────────────────────────────────────\n# Choices: \"efficientnet_b0\" | \"efficientnet_b2\" | \"mobilenet_v3_small\" | \"resnet50\"\nMODEL_NAME = \"efficientnet_b0\"\nFREEZE_BACKBONE = False # True = only train the head (faster, good for small datasets)\n\n# ── Training hyperparameters ───────────────────────────────────────────────────\nEPOCHS = 50\nBATCH_SIZE = 64\nIMG_SIZE = 224\nLR = 1e-3\nWEIGHT_DECAY = 1e-4\nPATIENCE = 10 # early-stop if val accuracy does not improve for this many epochs\nNUM_WORKERS = 2 # Kaggle kernels: keep ≤ 2 to avoid DataLoader warnings\nUSE_AMP = True # Automatic Mixed Precision — speeds up training on GPU\nSEED = 42\n\n# ── Sanity check ───────────────────────────────────────────────────────────────\nassert (DATA_DIR / \"train\").exists(), f\"train/ not found inside {DATA_DIR}\"\nassert (DATA_DIR / \"val\").exists(), f\"val/ not found inside {DATA_DIR}\"\nprint(f\"✓ DATA_DIR : {DATA_DIR}\")\nprint(f\"✓ OUTPUT_DIR : {OUTPUT_DIR}\")\nprint(f\"✓ Model : {MODEL_NAME} | Freeze backbone: {FREEZE_BACKBONE}\")\nprint(f\"✓ Epochs : {EPOCHS} | Batch: {BATCH_SIZE} | LR: {LR} | AMP: {USE_AMP}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:05.262104Z","iopub.execute_input":"2026-05-11T09:57:05.262798Z","iopub.status.idle":"2026-05-11T09:57:05.269486Z","shell.execute_reply.started":"2026-05-11T09:57:05.262772Z","shell.execute_reply":"2026-05-11T09:57:05.268851Z"}},"outputs":[{"name":"stdout","text":"✓ DATA_DIR : /kaggle/input/datasets/triztechdata/product-dataset/crops_dataset\n✓ OUTPUT_DIR : /kaggle/working/runs/classify\n✓ Model : efficientnet_b0 | Freeze backbone: False\n✓ Epochs : 50 | Batch: 64 | LR: 0.001 | AMP: True\n","output_type":"stream"}],"execution_count":17},{"id":"diag0001","cell_type":"code","source":"# ── Cell 2b — Dataset diagnostics (run before training) ────────────────\n# Prints per-class image counts and flags classes with too few samples.\n# A val loss stuck at ~5 almost always means imbalance or too few images.\nfrom pathlib import Path as _Path\nfrom collections import Counter\nimport math\n\ndef diagnose_split(split: str):\n split_dir = DATA_DIR / split\n if not split_dir.exists():\n print(f' {split}/ not found')\n return {}\n counts = {}\n for cls in sorted(split_dir.iterdir()):\n if cls.is_dir():\n n = sum(1 for f in cls.iterdir() if f.suffix.lower() in {'.jpg','.jpeg','.png'})\n counts[cls.name] = n\n return counts\n\ntrain_c = diagnose_split('train')\nval_c = diagnose_split('val')\n\nprint(f'{\"-\"*60}')\nprint(f' {\"Class\":<28} {\"Train\":>7} {\"Val\":>7} Notes')\nprint(f'{\"-\"*60}')\nall_classes = sorted(set(train_c) | set(val_c))\nfor cls in all_classes:\n tr = train_c.get(cls, 0)\n vl = val_c.get(cls, 0)\n notes = []\n if tr < 20: notes.append('⚠ train<20')\n if vl < 5: notes.append('⚠ val<5')\n if tr == 0: notes.append('❌ missing in train!')\n if vl == 0: notes.append('❌ missing in val!')\n print(f' {cls:<28} {tr:>7} {vl:>7} {\" \".join(notes)}')\nprint(f'{\"-\"*60}')\nprint(f' {\"TOTAL\":<28} {sum(train_c.values()):>7} {sum(val_c.values()):>7}')\nprint(f'{\"-\"*60}')\n\nif train_c:\n ratio = max(train_c.values()) / max(min(train_c.values()), 1)\n print(f'\\n Imbalance ratio : {ratio:.1f}x')\n print(f' Random-chance loss (ln {len(all_classes)}) : {math.log(len(all_classes)):.3f}')\n print(f' If val loss ≈ {math.log(len(all_classes)):.1f} and flat -> model is guessing randomly')\n print(f' If val loss > {math.log(len(all_classes)):.1f} -> model is confidently wrong')\n if ratio > 5:\n print('\\n ⚠ Imbalance >5x detected — WeightedRandomSampler is already enabled in Cell 5.')\n if min(train_c.values()) < 20:\n print('\\n ⚠ Some classes have <20 training images.')\n print(' Recommended minimums: train>=50, val>=10 per class.')\n print(' Consider: --freeze_backbone True (head-only training) for small datasets.')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:07.521025Z","iopub.execute_input":"2026-05-11T09:57:07.521716Z","iopub.status.idle":"2026-05-11T09:57:07.573070Z","shell.execute_reply.started":"2026-05-11T09:57:07.521685Z","shell.execute_reply":"2026-05-11T09:57:07.572492Z"}},"outputs":[{"name":"stdout","text":"------------------------------------------------------------\n Class Train Val Notes\n------------------------------------------------------------\n 1001 231 43 \n 1001 Gold 104 20 \n A100 26 5 \n Aroma 60 11 \n Aroma Espresso 105 20 \n Aroma Familial 80 15 \n Aroma Gold 40 8 \n Bonal 106 20 \n Bonal Excellence 43 8 \n Cafe D Or Caps 72 14 \n Canastel 24 4 ⚠ val<5\n Caps 36 7 \n CityOne Instant Coffee 30 6 \n Dozia 29 6 \n El Kabir 37 7 \n Facto 82 15 \n Facto no 28 5 \n Famico 490 92 \n Famico Caps 114 21 \n Famico Exclusive 175 33 \n Molino 131 25 \n Mundo 55 10 \n Mundo Caps 61 11 \n Nizier 97 18 \n Nouara 28 5 \n Oscar 83 16 \n Primo 173 33 \n Ricamar Thon 128 24 \n Siglo 30 6 \n Skor 45 9 \n------------------------------------------------------------\n TOTAL 2743 517\n------------------------------------------------------------\n\n Imbalance ratio : 20.4x\n Random-chance loss (ln 30) : 3.401\n If val loss ≈ 3.4 and flat -> model is guessing randomly\n If val loss > 3.4 -> model is confidently wrong\n\n ⚠ Imbalance >5x detected — WeightedRandomSampler is already enabled in Cell 5.\n","output_type":"stream"}],"execution_count":18},{"id":"d4e5f6a7","cell_type":"code","source":"# ── Cell 3 — Imports ─────────────────────────────────────────────────────────\nimport json\nimport time\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torch.cuda.amp import GradScaler, autocast\nfrom torchvision import datasets, models, transforms\nfrom torchvision.models import (\n EfficientNet_B0_Weights,\n EfficientNet_B2_Weights,\n MobileNet_V3_Small_Weights,\n ResNet50_Weights,\n)\nimport matplotlib.pyplot as plt\nimport matplotlib\nfrom tqdm.notebook import tqdm # notebook-aware progress bars\nfrom IPython.display import display # for inline plot updates\n\nmatplotlib.rcParams[\"figure.dpi\"] = 120\ntorch.manual_seed(SEED)\nOUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n\nDEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(f\"Running on: {DEVICE}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:09.271527Z","iopub.execute_input":"2026-05-11T09:57:09.272154Z","iopub.status.idle":"2026-05-11T09:57:09.279891Z","shell.execute_reply.started":"2026-05-11T09:57:09.272127Z","shell.execute_reply":"2026-05-11T09:57:09.279038Z"}},"outputs":[{"name":"stdout","text":"Running on: cuda\n","output_type":"stream"}],"execution_count":19},{"id":"e5f6a7b8","cell_type":"code","source":"# ── Cell 4 — Dataset exploration ─────────────────────────────────────────────\nimport os\nfrom collections import Counter\n\ndef count_split(split_dir: Path):\n \"\"\"Return {class_name: image_count} for one split directory.\"\"\"\n counts = {}\n for cls_dir in sorted(split_dir.iterdir()):\n if cls_dir.is_dir():\n n = sum(1 for f in cls_dir.iterdir() if f.suffix.lower() in\n {\".jpg\", \".jpeg\", \".png\", \".bmp\", \".webp\"})\n counts[cls_dir.name] = n\n return counts\n\ntrain_counts = count_split(DATA_DIR / \"train\")\nval_counts = count_split(DATA_DIR / \"val\")\n\nprint(f\"Classes found : {len(train_counts)}\")\nprint(f\"Train images : {sum(train_counts.values()):,}\")\nprint(f\"Val images : {sum(val_counts.values()):,}\")\nprint()\n\n# Bar chart of class distribution\nfig, axes = plt.subplots(1, 2, figsize=(14, max(4, len(train_counts) * 0.35)))\nfor ax, counts, title in zip(axes, [train_counts, val_counts], [\"Train\", \"Val\"]):\n classes = list(counts.keys())\n values = list(counts.values())\n ax.barh(classes, values, color=\"steelblue\")\n ax.set_title(f\"{title} split ({sum(values):,} images)\")\n ax.set_xlabel(\"Image count\")\n for i, v in enumerate(values):\n ax.text(v + 0.5, i, str(v), va=\"center\", fontsize=8)\nplt.tight_layout()\nplt.savefig(OUTPUT_DIR / \"class_distribution.png\", bbox_inches=\"tight\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:11.605993Z","iopub.execute_input":"2026-05-11T09:57:11.606635Z","iopub.status.idle":"2026-05-11T09:57:12.776952Z","shell.execute_reply.started":"2026-05-11T09:57:11.606607Z","shell.execute_reply":"2026-05-11T09:57:12.776080Z"}},"outputs":[{"name":"stdout","text":"Classes found : 30\nTrain images : 2,743\nVal images : 517\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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── Cell 5 — Data loaders + balanced sampler ────────────────────────────────\nimport numpy as np\nfrom torch.utils.data import WeightedRandomSampler\n\nMEAN = [0.485, 0.456, 0.406]\nSTD = [0.229, 0.224, 0.225]\n\ntrain_tf = transforms.Compose([\n transforms.RandomResizedCrop(IMG_SIZE, scale=(0.7, 1.0)),\n transforms.RandomHorizontalFlip(),\n transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.05),\n transforms.RandomGrayscale(p=0.05),\n transforms.ToTensor(),\n transforms.Normalize(MEAN, STD),\n])\nval_tf = transforms.Compose([\n transforms.Resize(int(IMG_SIZE * 1.15)),\n transforms.CenterCrop(IMG_SIZE),\n transforms.ToTensor(),\n transforms.Normalize(MEAN, STD),\n])\n\ntrain_set = datasets.ImageFolder(str(DATA_DIR / 'train'), transform=train_tf)\nval_set = datasets.ImageFolder(str(DATA_DIR / 'val'), transform=val_tf)\n\nCLASS_NAMES = train_set.classes\nNUM_CLASSES = len(CLASS_NAMES)\n\n# ── Weighted sampler: every class gets equal expected frequency ───────────\n# This prevents dominant classes from drowning out rare ones.\n# If your classes are already balanced, this is a no-op in effect.\ntargets = train_set.targets # list of int labels\nclass_counts = np.bincount(targets) # images per class\nclass_weights = 1.0 / class_counts # inverse frequency\nsample_weights = class_weights[targets] # weight per sample\n\nsampler = WeightedRandomSampler(\n weights = sample_weights,\n num_samples = len(train_set),\n replacement = True, # allows oversampling rare classes\n)\n\ntrain_loader = DataLoader(train_set, batch_size=BATCH_SIZE, sampler=sampler,\n num_workers=NUM_WORKERS, pin_memory=True)\nval_loader = DataLoader(val_set, batch_size=BATCH_SIZE, shuffle=False,\n num_workers=NUM_WORKERS, pin_memory=True)\n\n# ── Class imbalance report ──────────────────────────────────────────\nprint(f'Classes ({NUM_CLASSES}):')\nmax_count = class_counts.max()\nfor i, (cls, cnt) in enumerate(zip(CLASS_NAMES, class_counts)):\n bar = '█' * int(20 * cnt / max_count)\n flag = ' ⚠ LOW' if cnt < 20 else ''\n print(f' {cls:<28} {cnt:>5} {bar}{flag}')\nprint(f' Imbalance ratio: {max_count / class_counts.min():.1f}x '\n f'(max {max_count} / min {class_counts.min()})')\n\n# ── Save class mapping ─────────────────────────────────────────────\nclass_map = {i: c for i, c in enumerate(CLASS_NAMES)}\nwith open(OUTPUT_DIR / 'class_names.json', 'w') as f:\n json.dump(class_map, f, indent=2)\nprint(f'\\n✓ class_names.json saved')\n\n# ── Sample grid ────────────────────────────────────────────────────\nimport torchvision\nsample_imgs, _ = next(iter(train_loader))\nsample_imgs = sample_imgs[:16]\ninv_mean = torch.tensor(MEAN).view(3,1,1)\ninv_std = torch.tensor(STD).view(3,1,1)\ngrid_imgs = torch.clamp(sample_imgs * inv_std + inv_mean, 0, 1)\ngrid = torchvision.utils.make_grid(grid_imgs, nrow=8, padding=2)\nplt.figure(figsize=(14, 4))\nplt.imshow(grid.permute(1, 2, 0).numpy())\nplt.title('Sample training crops (augmented)')\nplt.axis('off')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:22.140018Z","iopub.execute_input":"2026-05-11T09:57:22.140697Z","iopub.status.idle":"2026-05-11T09:57:24.296513Z","shell.execute_reply.started":"2026-05-11T09:57:22.140667Z","shell.execute_reply":"2026-05-11T09:57:24.295585Z"}},"outputs":[{"name":"stdout","text":"Classes (30):\n 1001 231 █████████\n 1001 Gold 104 ████\n A100 26 █\n Aroma 60 ██\n Aroma Espresso 105 ████\n Aroma Familial 80 ███\n Aroma Gold 40 █\n Bonal 106 ████\n Bonal Excellence 43 █\n Cafe D Or Caps 72 ██\n Canastel 24 \n Caps 36 █\n CityOne Instant Coffee 30 █\n Dozia 29 █\n El Kabir 37 █\n Facto 82 ███\n Facto no 28 █\n Famico 490 ████████████████████\n Famico Caps 114 ████\n Famico Exclusive 175 ███████\n Molino 131 █████\n Mundo 55 ██\n Mundo Caps 61 ██\n Nizier 97 ███\n Nouara 28 █\n Oscar 83 ███\n Primo 173 ███████\n Ricamar Thon 128 █████\n Siglo 30 █\n Skor 45 █\n Imbalance ratio: 20.4x (max 490 / min 24)\n\n✓ class_names.json saved\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":21},{"id":"a7b8c9d0","cell_type":"code","source":"# ── Cell 6 — Model factory ───────────────────────────────────────────────────\ndef build_model(name: str, num_classes: int, freeze_backbone: bool) -> nn.Module:\n if name == \"efficientnet_b0\":\n m = models.efficientnet_b0(weights=EfficientNet_B0_Weights.IMAGENET1K_V1)\n if freeze_backbone:\n for p in m.features.parameters():\n p.requires_grad = False\n m.classifier[1] = nn.Linear(m.classifier[1].in_features, num_classes)\n\n elif name == \"efficientnet_b2\":\n m = models.efficientnet_b2(weights=EfficientNet_B2_Weights.IMAGENET1K_V1)\n if freeze_backbone:\n for p in m.features.parameters():\n p.requires_grad = False\n m.classifier[1] = nn.Linear(m.classifier[1].in_features, num_classes)\n\n elif name == \"mobilenet_v3_small\":\n m = models.mobilenet_v3_small(weights=MobileNet_V3_Small_Weights.IMAGENET1K_V1)\n if freeze_backbone:\n for p in m.features.parameters():\n p.requires_grad = False\n m.classifier[3] = nn.Linear(m.classifier[3].in_features, num_classes)\n\n elif name == \"resnet50\":\n m = models.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)\n if freeze_backbone:\n for pname, p in m.named_parameters():\n if \"fc\" not in pname:\n p.requires_grad = False\n m.fc = nn.Linear(m.fc.in_features, num_classes)\n\n else:\n raise ValueError(f\"Unknown model: {name}\")\n\n return m\n\n\nmodel = build_model(MODEL_NAME, NUM_CLASSES, FREEZE_BACKBONE).to(DEVICE)\n\ntrainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\ntotal = sum(p.numel() for p in model.parameters())\nprint(f\"Model : {MODEL_NAME}\")\nprint(f\"Total params : {total:,}\")\nprint(f\"Trainable : {trainable:,} ({trainable/total*100:.1f}%)\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:31.018408Z","iopub.execute_input":"2026-05-11T09:57:31.019160Z","iopub.status.idle":"2026-05-11T09:57:31.165229Z","shell.execute_reply.started":"2026-05-11T09:57:31.019125Z","shell.execute_reply":"2026-05-11T09:57:31.164618Z"}},"outputs":[{"name":"stdout","text":"Model : efficientnet_b0\nTotal params : 4,045,978\nTrainable : 4,045,978 (100.0%)\n","output_type":"stream"}],"execution_count":22},{"id":"b8c9d0e1","cell_type":"code","source":"# ── Cell 7 — Optimiser, scheduler, loss ──────────────────────────────────────\ncriterion = nn.CrossEntropyLoss(label_smoothing=0.1)\n\noptimizer = torch.optim.AdamW(\n filter(lambda p: p.requires_grad, model.parameters()),\n lr=LR,\n weight_decay=WEIGHT_DECAY,\n)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\nscaler = GradScaler() if (USE_AMP and DEVICE.type == \"cuda\") else None\n\nprint(f\"Optimiser : AdamW lr={LR} weight_decay={WEIGHT_DECAY}\")\nprint(f\"Scheduler : CosineAnnealingLR T_max={EPOCHS}\")\nprint(f\"AMP : {'enabled' if scaler else 'disabled'}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:34.338960Z","iopub.execute_input":"2026-05-11T09:57:34.339401Z","iopub.status.idle":"2026-05-11T09:57:34.347764Z","shell.execute_reply.started":"2026-05-11T09:57:34.339372Z","shell.execute_reply":"2026-05-11T09:57:34.346876Z"}},"outputs":[{"name":"stdout","text":"Optimiser : AdamW lr=0.001 weight_decay=0.0001\nScheduler : CosineAnnealingLR T_max=50\nAMP : enabled\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_57/958891556.py:10: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n scaler = GradScaler() if (USE_AMP and DEVICE.type == \"cuda\") else None\n","output_type":"stream"}],"execution_count":23},{"id":"c9d0e1f2","cell_type":"code","source":"# ── Cell 8 — Training helpers ────────────────────────────────────────────────\ndef run_epoch(model, loader, criterion, optimizer, device, scaler, train: bool):\n model.train(train)\n total_loss, correct, total = 0.0, 0, 0\n\n ctx = torch.enable_grad() if train else torch.no_grad()\n with ctx:\n desc = \" train\" if train else \" val \"\n for imgs, labels in tqdm(loader, desc=desc, leave=False):\n imgs, labels = imgs.to(device), labels.to(device)\n\n with autocast(enabled=(scaler is not None)):\n logits = model(imgs)\n loss = criterion(logits, labels)\n\n if train:\n optimizer.zero_grad(set_to_none=True)\n if scaler:\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n else:\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item() * imgs.size(0)\n correct += (logits.argmax(1) == labels).sum().item()\n total += imgs.size(0)\n\n return total_loss / total, correct / total\n\n\ndef plot_live(history, fig, axes):\n \"\"\"Redraw training curves in-place so they update each epoch.\"\"\"\n epochs = range(1, len(history[\"train_loss\"]) + 1)\n for ax in axes:\n ax.cla()\n\n axes[0].plot(epochs, history[\"train_loss\"], \"b-o\", ms=3, label=\"Train\")\n axes[0].plot(epochs, history[\"val_loss\"], \"r-o\", ms=3, label=\"Val\")\n axes[0].set_title(\"Loss\"); axes[0].set_xlabel(\"Epoch\")\n axes[0].legend(); axes[0].grid(alpha=0.3)\n\n axes[1].plot(epochs, history[\"train_acc\"], \"b-o\", ms=3, label=\"Train\")\n axes[1].plot(epochs, history[\"val_acc\"], \"r-o\", ms=3, label=\"Val\")\n axes[1].set_title(\"Accuracy\"); axes[1].set_xlabel(\"Epoch\")\n axes[1].set_ylim(0, 1); axes[1].legend(); axes[1].grid(alpha=0.3)\n\n fig.tight_layout()\n display(fig)\n plt.pause(0.01)\n\nprint(\"✓ Training helpers defined\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:37.745553Z","iopub.execute_input":"2026-05-11T09:57:37.745970Z","iopub.status.idle":"2026-05-11T09:57:37.756276Z","shell.execute_reply.started":"2026-05-11T09:57:37.745942Z","shell.execute_reply":"2026-05-11T09:57:37.755381Z"}},"outputs":[{"name":"stdout","text":"✓ Training helpers defined\n","output_type":"stream"}],"execution_count":24},{"id":"d0e1f2a3","cell_type":"code","source":"# ── Cell 9 — Training loop ───────────────────────────────────────────────────\nfrom IPython.display import clear_output\n\nhistory = {\"train_loss\": [], \"train_acc\": [], \"val_loss\": [], \"val_acc\": []}\nbest_val_acc = 0.0\npatience_ctr = 0\n\nfig, axes = plt.subplots(1, 2, figsize=(12, 4))\nplt.ion() # interactive mode so curves update live\n\nepoch_bar = tqdm(range(1, EPOCHS + 1), desc=\"Epochs\", unit=\"ep\")\n\nfor epoch in epoch_bar:\n t0 = time.time()\n\n train_loss, train_acc = run_epoch(model, train_loader, criterion,\n optimizer, DEVICE, scaler, train=True)\n val_loss, val_acc = run_epoch(model, val_loader, criterion,\n None, DEVICE, None, train=False)\n scheduler.step()\n elapsed = time.time() - t0\n\n history[\"train_loss\"].append(train_loss)\n history[\"train_acc\"].append(train_acc)\n history[\"val_loss\"].append(val_loss)\n history[\"val_acc\"].append(val_acc)\n\n improved = val_acc > best_val_acc\n\n # ── Epoch summary line ───────────────────────────────────────────────────\n epoch_bar.set_postfix({\n \"tr_loss\": f\"{train_loss:.4f}\",\n \"tr_acc\": f\"{train_acc:.4f}\",\n \"val_acc\": f\"{val_acc:.4f}\",\n \"best\": f\"{best_val_acc:.4f}\",\n \"⏱\": f\"{elapsed:.1f}s\",\n })\n\n # ── Save checkpoints ─────────────────────────────────────────────────────\n ckpt = {\n \"epoch\": epoch,\n \"model_state\": model.state_dict(),\n \"class_names\": CLASS_NAMES,\n \"val_acc\": val_acc,\n \"args\": {\n \"model\": MODEL_NAME,\n \"img_size\": IMG_SIZE,\n \"freeze_backbone\": FREEZE_BACKBONE,\n },\n }\n\n if improved:\n best_val_acc = val_acc\n patience_ctr = 0\n torch.save(ckpt, OUTPUT_DIR / \"best.pt\")\n else:\n patience_ctr += 1\n\n torch.save(ckpt, OUTPUT_DIR / \"last.pt\")\n\n # ── Live curve update ────────────────────────────────────────────────────\n clear_output(wait=True)\n plot_live(history, fig, axes)\n\n # ── Early stopping ───────────────────────────────────────────────────────\n if patience_ctr >= PATIENCE:\n print(f\"\\n⚠ Early stopping — no improvement for {PATIENCE} epochs\")\n break\n\nplt.ioff()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T09:57:53.181090Z","iopub.execute_input":"2026-05-11T09:57:53.181793Z","iopub.status.idle":"2026-05-11T10:02:32.653121Z","shell.execute_reply.started":"2026-05-11T09:57:53.181761Z","shell.execute_reply":"2026-05-11T10:02:32.652436Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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Early stopping — no improvement for 10 epochs\n","output_type":"stream"},{"execution_count":25,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}],"execution_count":25},{"id":"e1f2a3b4","cell_type":"code","source":"# ── Cell 10 — Final training curves ─────────────────────────────────────────\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\nepochs_x = range(1, len(history[\"train_loss\"]) + 1)\n\nax1.plot(epochs_x, history[\"train_loss\"], \"b-o\", ms=4, label=\"Train\")\nax1.plot(epochs_x, history[\"val_loss\"], \"r-o\", ms=4, label=\"Val\")\nax1.set_title(\"Loss\"); ax1.set_xlabel(\"Epoch\"); ax1.legend(); ax1.grid(alpha=0.3)\n\nax2.plot(epochs_x, history[\"train_acc\"], \"b-o\", ms=4, label=\"Train\")\nax2.plot(epochs_x, history[\"val_acc\"], \"r-o\", ms=4, label=\"Val\")\nax2.set_title(\"Accuracy\"); ax2.set_xlabel(\"Epoch\")\nax2.set_ylim(0, 1); ax2.legend(); ax2.grid(alpha=0.3)\n\nfig.tight_layout()\nfig.savefig(OUTPUT_DIR / \"training_curves.png\", dpi=150)\nplt.show()\n\nprint(f\"\\n{'═'*50}\")\nprint(f\" Training complete!\")\nprint(f\" Best val accuracy : {best_val_acc:.4f}\")\nprint(f\" Epochs trained : {len(history['train_loss'])}\")\nprint(f\" Outputs saved to : {OUTPUT_DIR}\")\nprint(f\"{'═'*50}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T10:04:07.052836Z","iopub.execute_input":"2026-05-11T10:04:07.053439Z","iopub.status.idle":"2026-05-11T10:04:07.722352Z","shell.execute_reply.started":"2026-05-11T10:04:07.053408Z","shell.execute_reply":"2026-05-11T10:04:07.721517Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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fTTS9TL7N27t44dO6YtW7YEqGUAAABAUXWizMWhQ4f01VdfqW3btoqIiAh0cwAAABDEcnNztWPHDp133nlq2rRpoJuj9PR09e/fv8T2uLg4SdLu3bvVvXt3r8dmZGRo3759RbZlZ2dry5Yt6t69O++NAQAAUKaKvjeuE2HyV199pQEDBgS6GQAAAKhFli5dquuuuy7QzVBOTo7X0DcyMrLg9tLMmDFDkydPrrG2AQAAoH7w9b1xnQiT27ZtK8k86U6dOgW4NYGVn5+vrKwsRUdHKywsLNDNCWr0lW/oJ9/QT76jr3xDP/mOvvIN/eSRnJysAQMGFLyHDLSoqCivdZGPHz9ecHtpxo4dq0GDBhXZ9vvvv+vGG2/U4sWLlZCQUL2NrWWcTmfB773dbg90c4JaMPXVhx/aNGWKXS6XrWBbSIilxx5z6rrrrIC1a/t26YMPpG3bHGrfPlTXXy+1axeYtmzbJt1wQ6hcLkmyFbrFks0mjR/vVKNG0vHjNuXmSsePS7m5OrHubZuUm2sr2Fb4tqL3j8qzFBoqhYVJTqeUl1d6v9rt5vfc6aTvgfrusstceuYZZ40+RkpKigYOHOjze+M6ESa7R3J06tRJXbt2DXBrAis/P1+ZmZlq3rx5vf+gWB76yjf0k2/oJ9/RV76hn3xHX/mGfiopWEpAxMXFKT09vcR297Y2bdqUemxsbKxiY2O93talSxfeG/N777Ng6Kv8fGnNGmnKFMkqlhlbltnepYvUubMUFVVyCa3BT7fz5kmjRpkQ0G3hQmnWLGn48IrdV36+lJ0tZWWZxdt6ebcfOlT6/VuW9MILlXqaNcpulyIiJJvNLJJn3Zelsvvv3SsdOVJ6u0JDpZAQ83Mp/ntX3RwOs5THWbO5UQF3uB0eLuXkSHl5pe/bpIn58qS6fz5lLcnJUmpq6W1q3VqKizM/O4fDXBZeL77NX/1aU0JCzM/MbjeX7sVuN68LZZzIpOhoqW3bmv97q8jP79RTpT//ueRzqu71wttmzZLefLP0Ng0ZIo0ebX5XXC5zWXgpvq06rn/5pbR2beltOu00qVev0m+vDu7/+76+N64TYTIAAABQW/Xq1UurVq2Sy+UqMgnf2rVr1aBBA3Xu3DmArUN9kJQkzZoVoqSkaCUmhmjUKCkxsXru2+WSMjOlPXs8y969Ra+7t+3fX/r9WJZZbr659H3CwryHzFFRUoMGFdte+LZ9+6Tbby8ZNLpcZvuRI2Y/XwPisgKfmhQe7vtzrsxtL78szZhR+uM/+KD09NP+e75uEydKzzxT+u0PPGDaZVkm3MnPN6FqTV6uXCn99FPpberdW7r4Yk/QW12XhdfdIbqv/TR2rP9/fklJ0imnmL+1wn9/Nptp+6pVUkVOTncHeeWFzmVtmztXev/90h/j+uulYcNqJhh1h7belPfz+9vfgu/n9+GHFfv5VYe4OOmtt0pv0+OP+79N5fXTyJH+bY8vCJMBAAAAP0lPT1dWVpYSEhIKRoEMHDhQixcv1pIlSzRw4EBJ0v79+/Xee+/pmmuuCZoR1Ki6pCQTRKSlSfHx0ogR1RfaVpZnxK1dkimp8sILZY+4tSwTinoLhotfz8jw32hAd/CTne2fx3MH3PfcU/33HR5uRhI2aWIuC683aWKCyNWrSz9+1Chp0iRP2BsZacKomjRunPT668EXiIwYIU2bVn67bDZPcFdGdaFqUV549O9/+z/Q8rWf/Ckx0bwWFT8rICREmj274n0UEmKWqpx8kZgoLV1aej8995z/f3ZS/fj50abgQZgMAAAAVIPp06fr0KFD2r17tyRp+fLl2rlzpyTp7rvvVnR0tCZOnKgFCxYoNTVV8fHxkkyY3LdvXw0fPly///67WrRooRkzZsjpdDK5Xh3irUzCtGmVK5NQXZKSTJtM3V0P94jb7dvNureg2EuZ7wqJiDAjxFq1Mqequ5fvvpP+97/Sj7vxRumWW6Rjx8wIX29LWbd5u726Sxs0blwy/C0eCJe3fmL+zVK5w8iOziQN11zFK01pitc8jdBWe6ImTDCntPtTsAYiwdgu2uS74cOlfv2kmTOdSkrKU2JiuEaPtvP7VEva5f75zZnj+SJ15MjABqTB9jtVuE3B1E9lIUwGAAAAqsHzzz+vbdu2FVxfsmSJlixZIkkaMmSIoqOjvR5nt9v1ySef6MEHH9Qrr7yinJwcnXnmmZo/f75OPvlkv7QdNau80DYhQWrTxgS0eXmey8LrNbFtzx7vo4bdI26feKJizzM0VIqNLRoOFw+L3debNPF+ynZ5IenUqdX74dqyTH+UFTbPnCmd+FP2auRI6bHHTBDcqFHNjwCWTHD05dB5OmveKIXK80N8UNO05rZZ6tQpMN9QDB8uXdg2SSmPzlbYzlTl/6mDEp68Xe0vDuwQfHe7UifNVfjuNOW1iVeHv48IaLsItHzXyUrS05qlPCUpXImyW6MkBf5nF2z9FIy/U5Lpl0CUuClLp07S1KkuZWZmnZgrIPCT9AZjP5XGZlk1XWK+5m3cuFHdunXTb7/9xiQjQTBxRm1BX/mGfvIN/eQ7+so39JPvgqWvHA6HDh48qCNHjigY3165XC7l5eUpPDy8SF3eusJmsykiIkJNmjRRw4YNZSujsGBdf+9Y15+fr0wdYM8H6lGj7NVeUiI319QDPnDAsxS+7l7/4Qczyrc2stmkFi3KDobdS0xM0RqslbVqRMmQ1CG71oyYpXPn+D8kLa8kwebNAQiRTjTKcrlkK9Qoy2aTLSRE2rQpMDVUvA3Bt9sDOwQ/mNul4HkfUyDY6vEE8c8uWAXd71QwSkqSc9Ys5SUlKTwxUfbqnCygCm0K1N9eRd87MjIZAACgiizL0s6dO5WTkyO73a7Q0OB7i2Wz2RQeHl5myFqbOZ1OZWVlKSsrSzExMYqNja2zzxXlq2gd4NzcssPg0taPHfPv8yqN3W5q7EZEeCbZcq+XtW3jRrOU5q67TL/5NYtISlK/N0bJsrmkQsGt3ebSuQtGSRP7+T25DarTxy1LSk6W7r9fcjpV/FXO5p5FrnNn84sRFlZ0CQ0tuc2X23w59tAhU7ul+BeqTqcZgp+RYb6B8Lc9e6RHHvF+asCoUWYoZ6CHbgaLQNXjcTiKnhZw7JhZtmwpffZLfnaorBO/53anUwXl0cubLMBPbQqqWlhlCL5POgAAALXM4cOHlZOTo+joaMXFxQVliOlyueR0OmW32+vkyGRJysvLU3p6ug4cOKCGDRuqUaNGgW4SAqC0khJOpzkN+d13i4bHBw5IR49WfztCQ81I3ebNzYRwu3aVvu8VV5g6wL4EwN5uq2xZhfJG3N57r5+CZMuSUlOlr74yH+jLCkmHD5cmTJB69ZL+9CfvtTJqQMBOH9+9W/r+ezO8/fvvpXXrTGjrC6fTLMeP12gTfeJySf/3f4FuRVHu36kePUwg6R5iHxfnfT06uvp/35KSFDJrlqKTkhSSmGhevAI1OrK8ejzt2kktW5YMfEtbr8h++fkVa6v7Z3fffdK//iWddFL19QPqtvLeJHz+ufnHXZEv1CrzJV3h7du2lf63F6RfmhAmAwAAVFF2drYkMRo2wMLDwxUXF6eUlBRlZ2cTJtdTc+d6rwMsmfzhv/+t2P0VDoVjYkqul3Zbo0ae3Km80PaVV+rZhFuWZTrlq688y4nJKsv1zTdmkUxn9+olnXaa57JzZ/NDqwE1XmPz4EETFruD4x9+MGFyZZxzjnTeeSak87Y4HBW/rbTttV1OjvTrr2YpS2Rk0boupQXPrVqZb3nKU1OjIy3LfIGQnS1lZZW89LYtO1v65Zeyi6hffHHl21RTli83S0KC+X13L+3bB7plCCa5uWZ2188/L/9NwqJF/m1bWdxfmsyZE3TFlAmTAQAAqig/P1+hoaFBWd6ivgkPD1dYWJhyc3MD3RQESFpa2bc3aSJ17165ULiygqpMQjF+GXFrWaawcOHwOD3d+77h4WZGPF9kZkpffGEWt8hIM9K0cMjco4fUoEFVn0X1ysmR1q/3hMY//GAC9tLYbNKpp0pnnin17m0CyxtvLP0bivnz/fOL5Q47HA5TSuKFF0rf929/M6PK/e2558zo1dL06mVG3aanm5IYe/Z4D8mPHzcvMOW9yEjmxaO0Ec6tW5vf8bJGInboYO6jImFw4ctAhvwhIVLDhuZvLirKXBZf9/W2RYukt98u/zFTUswyd6653r69dP75nnC5Qwe/ncWAIOBymS9HPv/cLF9/bV5zfRERYX7/Cn+JVvzv1N98ec3xMz7xAAAAVJFlWXW2dERtZLPZgnISRPhHfHzZt48dG5gBPu7Qds4cz9w6I0cGx5mr1T7i1uUyxZjdwfHXX5t6ud40a2Y6xh36NGwodetWekj6ww/S4cMmiN2wwVxu3Gg+8Esm8Pv+e7O4hYSYEcuFRzD36mVO2fcHh8O0sXBw/OuvpY+Ok8wviDs4PvNM6fTTpcaNi+4TDN9Q2GxmJHhoqHTHHdLLL5f+s7vvvvL/QGvC+PHSzJmlt+u994r2l2WZUeKFw+U9e7xfP3DA+2O6a+iUVZTcG3c4f8EFFX+eFRUVZb5di442l/v2mdPtS3P11dKwYeWHwGFh1Rfcdulifj6l/ewWLTIh8ldfSd9+Kx05Ym7ftk1asMAskimJU3jkcmJi3Q2Xg6l0ij+lpprg2P0F4/793vdr1Urau7f0+xk/vuSbBJer+s7w8Hbb8uXSypWltykQr5vlIEyupGCb4BQAAAQW5S2CBz+L+m3ECOnZZ0vO1+TOHkaODEy7JJNXBdmZqtXD6TSjwNzh8apVZtSwNy1aSP37e0Kd7t3ND6awskLS004z1/v399yWmyv9/rsnXN6wwSyHD5vbXS4zMnrzZumttzzHnXRSyTIZZY1g9CWksSwTbhWuc7x+fdmj4lq29ITG7sWXoDvYvqEI1iH4FW2XzeY5RaFr17LvOzfXfFFSWthceL26zpix2z0hsDsI9uWy8HrjxiVLcZRXj+fFF/3/MyzvZ3fjjeb6xIkmoPvpp6KvQyfKkGnnTunf/zaLZEaKu1+Hzj/fhNZ14b1DME4sV1MyM6UVKzyjj7du9b5ffLx0ySWmTMsFF5ia82X9nnt7kxASYkYsR0TUxDORrrmm4m0KMMLkSqhlkywCAAAA9UbHjiav3Lev6PZA51l1isNhwtrCoU1pk8LFxhYdEXjqqSXD4+IqGpJGRJgg+LTTPB/IXC4zUm39+qKjmAuX19i1yywff+zZ1qSJCZYLh8ynnmpCKG8hzbRp5peu8AR5Bw+W/twaNZL+/GdPaNy7tymxUNkgK9i+oTjxs3POnKm8pCSFJybKPnp04P/waip4j4iQ2rY1S1ksy5SfcIfLL74oLVtW+v5XXmm+GfMWDDdoUDPBZ7B+GeDr71RoqPl76t1bevBB8xx+/tm8Rq1caV6n3H+b6enSO++YRTKvU4W/5OratfzXqUCyLPOaW/hLi19/9f5NqtNpJlA8+WTprLNqb2iek2Pq5bvD4/XrSz5XyXwJdNFFJjy++GLz+lxYy5bB93serH97ZbBZdeAcwI0bN6pbt2767bff1LW8bw6rqLwv6zZvDuzPOT8/X5mZmSdOT/PH1Mu1F33lG/rJN/ST7+gr39BPvguGvtp6YjREx+JvWIOIy+WS0+mU3W4PupIcw4YN08qVK5VWTTXhyvt5+PO9YyDU9edXnqVLpeuvN+sXXeRS48a5NVMHuC5JSpJz1ixPUFN8xG1+vvTjj57w+JtvPKN+i2vTpmh4fPLJwRVe7N3rGbnsDpm3bPEeSriFhnrKaFREWJgJowsHxyefbEaW1nHB8L85qAVzsJCcHDyj3Qup8u+Uy2UC18Lld0orhdC8edHyOz16lPy7rYnT1XNzzWtU8VHt3ka8+1pbvrCoKHP2RceOJZcOHYKrtrzTaUaau8Pjb7/1Pro/MtJ8EeAOj3v29O2LgOTk4PvSK4B/exV971jpkclHjhzRtGnTtHbtWn3//fc6ePCg5s2bp2HDhvl8H59//rmeeuop/fjjj3K5XOrcubMmTJigm266qbLNqnGlTfwYxJMsAgAAVIqv5SK+/PJLnX/++TXbGMBHL71kLqOjpffecyo3t5rqAFeHYKyVV9pp0RMmmFG0K1dKq1dLR496P75du6LhcUJCcIXHxbVqJV12mVncjhwxIVPhUcy//uoJLnwJkm02Ew4WrnPco0fNnRaN2i2YRyIG22j36hISYoLGnj2le+4x4fKmTUUnBnXX0s3MNN9MLl1qrjdtKp17rmdSvw0bTI1wX05Xtyxzf+XV396zp+yzGqpDTo4pCfT7795vb93ae9DcsaMpDVLZwQi+/O+zLBOmusPjFSu8n/ESEmLO7nCHx2edZQLliurUSa6pU5V14gsKezB86VWL/vYqHSbv379fU6ZMUbt27dSzZ0+tLKtYtBfz5s3TyJEjdckll+ipp56S3W7XH3/8oR07dlS2SX5R3oCZIJxkEQAAoFIWLlxY5Pobb7yhzz77rMT2U045pUqPM2vWLLkCPVM26oT1600eIJmMplGj6itTWmXBVCsvP9+ECr/9Zk5/9nZadGkfaDt08ATH558flBMDVVijRiaQOOssz7b8fOmPP8wv1dSpZr00F15oQqfiE+QBZQnWkiD1RUiIKWfRtauZmdWyzFkKK1d6wuXdu82+hw5JH31kltI4nWYk6X//a15f3QHx3r3m9aQqmjc3YW7r1p6l+PVZszzfpnpz8cXmuW7d6lmK13J3h9urV5c8PiKi7FHNjRp5f9yy/vddeaWZLM8dIJeWB3bu7AmPzz/fTNyKgKp0mBwXF6f09HS1bt1a69at05lnnunzsWlpabrzzjt199136+WXX65sEwKivPdKdeG9FAAAgCQNGTKkyPU1a9bos88+K7G9uGPHjqlBBU6V5BRoVJcXXzSXdrs0/uokhTwSJDPaJyWZxy/+pYnLZbb362dG9ObmSseOeZacnPLXK3NbRUo2JCYWHXlcXm3YuiIsTOrWzSy//y4980zp+/buTZCMygnG0ZH1lc1mytCcfLI0ZoxnMs3CI5e3by/7PixLevdd3x4vMrJoIFxaWBwbW3KyRG/GjpVefbX00in/+lfRLyosywTdW7ea+vKFQ+atW82khYXl5nomMvUmNrZkyBweXvoXliNGlP5cYmM94fFFF5kzYBBUKh0mR0REqHXr1pU69rXXXpPT6dSUKVMkmZIZDRs2rBUzb48YYb5EqUWTLAIAgFoqGM+IL+7888/X/v37tWDBAo0fP17r1q3T6NGj9dJLL+nDDz/UzJkztX79emVmZupPf/qThg0bpocfflj2QrUHi9dMTktLU4cOHTRt2jQ1adJEzz77rHbu3KkePXpoxowZFRrEgPojPV16+22z/urp89TmIjMSqsoz2judnhC2siHumjVl18o75RRzGWzT2Vx3nec07/qMD4FA/WOzmfC1UyfP33hamnTzzdLataUfFxFhAunyRhI3aVK9JYEqWjrFZvO05eyzS97f8ePStm0lQ2b3cuRI0f0zMsyyZk3F296wofmy0h0gd+sW3OWSUPkwuSo+//xzdenSRZ988okefPBB7dq1S82aNdOdd96pyZMnB92kMIUFc2kjAABQdwTTGfHlyczM1BVXXKGbb75ZQ4YMUatWrSRJ8+fPV6NGjXTfffepUaNGWrFihR577DFlZ2dr2rRp5d7vokWLdPjwYY0ZM0Y2m03PPfecbrjhBm3dupXRzChhxgxzJnEnJemOH0dJVrFRwO5TkFetMqOlfA2CKzPJUUVVZnI3t7AwM2lSgwZmciVv66Xd9r//SZ9+Wvp9V7GETZ3Bh0AAkvlm/4ILyg6Tx48PXN3b6iydEhnpGaldnGWZyQtLC5p37PDty9GuXc2I6T59fBt9jaARkDA5KSlJdrtdw4cP14QJE9SzZ08tWbJETz75pBwOh54u4w8vIyND+/btK7ItOTlZkpndM7+qtWh8MGSI1LevNGBAqLZssalpU0urVzvUqVPVS+FUlcPhkNPplKMqb0jrCfrKN/STb+gn39FXvqGffBcMfeVyuWSz2aqt7q85I9524ox4z8gMl8vSqFHSOedYlfpcYJ14Y1/ZdpZ2/J49ezRjxgyNGTOmUFtdevPNNxUVVTAuVKNHj1azZs00Y8YMTZkyRREnJqYqfr/uy+3bt+uPP/5QsxO18RITE3X99dfrP//5j66++uoy22lZVqnvC/3xfhH+lZMjvfaa1EwHNLfpfbId8jIKWDIfbufN80+j3IFtVJSZvK6siZV69zYTwpUX/npbD63CR7prrjGBMSNuy0d9WwBS8J+p4I/SKTab1LKlWfr0KXl7Xp4Z1fzoo2WX/bjmGlPmCbVOQMLkI0eOyOVy6ZlnntFDDz0kSfrLX/6iAwcO6OWXX9bDDz+sxqXUnJoxY4YmT57s9basrCxlZmbWWLsLa9ZMuvTSRtqypZGOHJGaNMmUnx66TA6HQ4cPH5ZlWQqtyhvLeoC+8g395Bv6yXf0lW/oJ98FQ1/l5eUpPDxcTi+nsd9/f4h+/rlip+qlptrkdJY8xrJscjrNGYAdOlT8dPju3UP0wguOSpcWc4e+hZ+nZVmKiIjQ0KFDSzz/wn1y+PBh5ebm6uyzz9bMmTO1ceNG9ezZ0+v9ui8HDRqkJk2aFFw/+8RpmCkpKV77unCb8vLySn1fmJWVVbEnjuCWlKRfHlmmxfuX6Rx9q9DSgmS30FApJsb30buVuS0ysuis90lJZYe2//53YEJJRtxWDPVtAfC6Wb7wcNNPTz4pvf9+8AbvqLSAfOKKiorS0aNHdcsttxTZfsstt+i///2v1q9fr/79+3s9duzYsRo0aFCRbcnJyRowYICio6PVvHnzGmt3caeeaj6IORw2HTvWPCgm33M4zAfEmJgYwody0Fe+oZ98Qz/5jr7yDf3ku2DoqyNHjshmsxWpA+z2yy82ff119dZ927HDph07KnefoaGVLyfmDqELP0+bzaaTTjqpyAhkt40bN2rSpEn68ssvlZ2dXeS2I0eOFNxP8ft1X7Zv377IY7Vo0UKSCYO99XXhNoWHh5f6vjA6OrrsJ4rg5nRK330nLVtmlj/+kJdxUaV74AH/n4IczOEDI24BoGJOvG5qzhzPxBYjR/K6WVww/+9DlQTkE1ebNm2UlJRUUE/PLTY2VpJ0sIxTwGJjYwv2Ky4sLMyv9fMKT4CzY0dY0EyIY7fbFRoaSi1BH9BXvqGffEM/+Y6+8g395LtA95V7vgdv8z706lXx+3OXmytN27ZmkuyKsdSjhyXJVun5Kdyhb/Hjo6KiSmw7dOiQLrjgAjVp0kRTpkxRQkKCIiMj9dNPPxWcmeY+pvj9ui9DQ0NLbWtZz8Fms8lms5X6+8DfVC10+LCp77tsmfTxx/J2SuBWddCBc6/Tn0edZk5DDraRUMEcPjDiFgAqplOnwNVGrk2C+X8fKi0gYfIZZ5yhpKQk7dq1Sx0LfRLavXu3JKlly5aBaFaFJSR41rdulS68MHBtAQAAwemllyp+THlnxK9YUfH34C6XJafTJan0Eb3VaeXKlcrMzNSSJUuKnHGWmprql8dHHbB9u7R8uVm+/LLkRHg2m9S3r+ZlXqvnt1yjtKhTtXOZTWomMwIqGEdCET4AAOob/vfVOZU/z9FH6enp2rx5c5GJTm666SZJ0pw5cwq2uVwuzZs3TzExMTrjjDNqulnVom1byX2GZUpKYNsCAADqDvdZgcUH3wZDFuYrdxkKq1AanpeXpxkzZgSqSQh2Lpe0bp302GPSaadJ7dtLd90lffqpJ0hu0EAaMECaO1fas0eb5qzWiC3/p9/VVcOG23RirkYzEmrzZjkffFA5AwbI+eCD0ubN0rBhAXpyAAAAdUOVRiZPnz5dhw4dKhhRvHz5cu3cuVOSdPfddys6OloTJ07UggULlJqaqvgTRYWvu+46XXTRRXr66ae1f/9+9ezZU0uXLtU333yj119/vWBm72AXGmre427dahYAAIDqUtvPCjz77LPVrFkz3Xbbbbrnnntks9m0cOHCIuEyoJwcM9R+2TLpo4+kE58rimjTxsz4fu210gUXmAnuTnj5Mc9u995b7DhKNwAAAFS7KoXJzz//vLZt21ZwfcmSJVqyZIkkaciQIaVObmKz2bR06VI9+uijeueddzR//nydfPLJevPNNzV48OCqNMnvEhIIkwEAQM2ozWcFNm/eXB999JHuv/9+Pfroo2rWrJmGDBmiiy66SJdddlmgm4dA2rvX1D1etkz67DPp2LGS+/TqZcLja6+VTj/dlLQoJjNTeuMNs3711VLnzjXbbAAAAFQxTE5LSyt3n/nz52v+/Pkltjdq1EgvvfSSXqpMIcEg4i75TJkLAABQ102fPl3Tp08vsm3lypWl7n/22Wfru+++K7G9+Ojk4u8V4+PjSx3BzMjmIJWUZEpPuIfRjxjhma3asqTffzfh8bJl0tq1RYuBS1J4uJmA5JprTDLcrl25DzlzphnYLEnjxlXnkwEAAEBpAjIBX13iDpMPHjRLQZ02AAAAoD6YN6/kZHfTpkn33WdqHS9bJnmbeLF5cxMcX3ONdOmlUuPGPj9kXp7k/l6je3cmwgYAAPAXwuQqSkjwrKemEiYDAACgHklKMkGyy1V0u9NpAuXiunQxpSuuuUY66yzPbNYVtHixp7zyuHFeq2AAAACgBhAmV5F7ZLJkSl2cfnrg2gIAAAD41dy5RUckF2ezSf37ewJkd+mLKrAs6cUXzXpsrHTrrVW+SwAAAPiIMLmKCofJTMIHAACAeqW8OVSuv156//1qfchvv5XWrTPrf/ubFBlZrXcPAACAMoQEugG1XXS0KfcmESYDAACgnomPL/v2zp2r/SHd83eHh5swGQAAAP5DmFwN3KOTU1IC2w4AAADAr0aMMHWPixctttnM9pEjq/XhUlOlDz4w67feKrVqVa13DwAAgHIQJlcDd5jMyGQAAADUK4mJ0qxZUkixjxUhIdLs2VKnTtX6cK++6pnrb9y4ar1rAAAA+ICaydUgIcFcbt8u5edLYWGBbQ8AAADgN8OHS/36SXPmmBrK8fFmRHI1B8nZ2SaflqQLLpB69qzWuwcAAIAPCJOrgXtkstNpAmV3uAwAAADUC506SU8/XaMPMW+edPiwWR8/vkYfCgAAAKWgzEU1cIfJEqUuAAAAgOrmdEovv2zWO3WSrroqsO0BAACorwiTq0HhkciEyQAAAED1Wr7cTL4nSffeW7JEMwAAAPyDt2HV4KSTPHWSU1IC2xYAAIBglZaWJpvNpvnz5we6KahlXnzRXDZtKg0bFsiWAAAA1G+EydXAbjfzjEiMTAYAAHXHtddeqwYNGuiwu1CtF4MHD1Z4eLgyMzP92DLUJz/9JH39tVkfNUpq1Ciw7QEAAKjPCJOribvUBWEyAACoKwYPHqycnBx98MEHXm8/duyYPvzwQ11++eVq3ry5n1uH+uKll8yl3S7ddVdAmwIAAFDvESZXE/ckfCkpkmUFti0AAADV4dprr1Xjxo21aNEir7d/+OGHOnr0qAYPHuznlqG+SE+X3n7brP/lL1K7doFtDwAAQH1HmFxN3GFydrZ04EBg2wIAAOqIpCRp4kTpllvMZVKSXx8+KipKN9xwg7744gtlZGSUuH3RokVq3Lixzj33XD3wwAPq3r27GjVqpCZNmuiKK67Qzz//7Nf2ou755z+l/HyzPn58YNsCAAAAwuRq4y5zIVHqAgAAVIN586RTTpGeecYMzXzmGXN93jy/NmPw4MFyOBx69913i2w/cOCAPv30U11//fVKT0/X0qVLdfXVV+uFF17Qgw8+qF9//VXnnXeedu/e7df2ou7IyZFee82s9+kj9e0b2PYAAACAMLnauEcmS6bUBQAAQKUlJZmZxlyuottdLrM9OdlvTbnwwgsVFxdXotTFe++9p/z8fA0ePFjdu3fXli1b9PTTT2v06NGaNGmSvvnmGx0/flxz5szxW1tRt7z5puSe15FRyQAAAMEhNNANqCs6dPCsMzIZAAAUGDdO2rChYsds3So5nSW3W5bZfuGFRb/J9oFNUkiPHp7ZzHxkt9t1880368UXX1RaWpri4+MlmRIXrVq10kUXXSS73V6wv9Pp1KFDh9SoUSOdfPLJ+umnnyr0eIBkftXdv6pt25p6yQAAAAg8wuRq0rixFBsrZWQQJgMAgEI2bJC++qp673PHDrNUgE2SrZKzBA8ePFgvvviiFi1apIcfflg7d+7UqlWrdM8998hut8vlcunll1/WjBkzlJqaKmehILx58+aVekzUb599Jv3+u1m/6y4plE8tAAAAQYG3ZdWoY0cTJlPmAgAAFOjVq+LHbN1adljctm2FRyZbkqwePWSreGt0xhlnqEuXLnrrrbf08MMP66233pJlWRo8eLAk6amnntKkSZM0YsQI/f3vf1dMTIxCQkI0btw4uYqX6gB88OKL5rJBA1PZBQAAAMGBMLkadeworVnDyGQAAFBIBctKSDI1k085xdRILjya2GaTQkKkFSukTp0qdJeWyyWX0yl7+bt6NXjwYE2aNEm//PKLFi1apMTERJ155pmSpMWLF+uCCy4oUR/50KFDatGiRSUfEfXVpk3Sf/9r1ocPl5o1C2x7AAAA4MEEfNUoIcFc7tgh5eUFti0AAKAWS0yUZs0ywXFhISHS7NkVDpKrg3sU8mOPPaYNGzYUXJdMXWWrWAmN9957T7t27fJrG1E3vPyyZ/2eewLXDgAAAJTEyORq5D7b1LKktDSpc+eANgcAANRmw4dL/fpJc+aYNxbx8dLIkQEJkiWpQ4cOOvvss/Xhhx9KUpEw+eqrr9aUKVM0fPhwnX322fr111/173//Wx0rWIoDyMyU3njDrF99Ne+nAQAAgg1hcjUq/Hlp61be/AIAgCrq1El6+ulAt6LA4MGDtXr1avXu3VudCoXaDz/8sI4ePapFixbpnXfe0emnn66PP/5Y//d//xfA1qI2ev11KSfHrI8fH9i2AAAAoCTC5GrkLnMhUTcZAADUPWPHjtXYsWNLbI+IiNDzzz+v559/vsj2lStXFrkeHx9fohwG4JaXJ/3zn2a9Rw/pggsC2x4AAACURM3kahQXJ0VEmPWUlMC2BQAAAKhN3ntP2r3brI8bZ+abBAAAQHAhTK5GISFShw5mnZHJAAAAgG8sS3rxRbMeGyvdcktg2wMAAADvCJOrmbvUBWEyAAAA4Jtvv5V+/NGs/+1vUmRkYNsDAAAA7wiTq5l7Er6UFDPCAgAAAEDZ3KOSw8NNmAwAAIDgRJhczdxh8tGj0r59gW0LAAAAEOxSU6WlS8364MFSq1YBbQ4AAADKQJhczdxlLiRKXQAAAADlefVVyeUy6/feG9i2AAAAoGyEydXMPTJZMqUuAABA/WBR3ypo8LOoPbKzpdmzzfqFF0o9ewa2PQAAACgbYXI169DBs87IZAAA6oeQkBA5nU5CzCBgWZacTqdsNlugmwIfzJ0rHT5s1sePD2xbAAAAUD7C5GrWoIEUF2fWGZkMAED9EBERIafTqYyMDALlAHI4HEpPT5fT6VSjRo0C3RyUw+mUXnnFrCcmSldeGdj2AAAAoHyhgW5AXdSxo5SezshkAADqi1atWik3N1cHDhxQVlaW7HZ70I2MtSxLlmXJZrMFXduqyrIsuVwuORwOSVKDBg3UrFmzALcK5Vm2zEy+J5laySEMcwEAAAh6vGWrAe66yYTJAADUDyEhIWrXrp2aNm2q8PDwoAxrLctSXl5enRw5bbPZFBoaqsaNG+ukk05Su3btFBrKmIlg9+KL5rJpU+m22wLaFAAAAPiId9k1ICHBXO7aJeXkSFFRgW0PAACoeSEhIYpz17oKQvn5+crMzFTz5s0VFhYW6ObUObm5uXrssce0cOFCHTx4UD169NCTTz6pSy65pNxjP//8c02dOlW//vqrHA6HOnfurLvvvlt//etf/dDywPjxR2nVKrM+apREVRIAAIDagZHJNcA9MlmS0tIC1gwAAAD4ybBhw/TCCy9o8ODBevnll2W323XllVfqm2++KfO4ZcuW6dJLL1VeXp6eeOIJTZ06VVFRURo6dKhedA/drYNeeslc2u3S3XcHtCkAAACoAEYm14DCYfLWrdIppwSuLQAAAKhZ33//vd5++21NmzZNDzzwgCRp6NCh6tatmyZMmKDVq1eXeuz06dMVFxenFStWKCIiQpI0ZswYdenSRfPnz9f48eP98hz8afdu6e23zfrAgVLbtoFtDwAAAHzHyOQa4C5zIUkpKYFrBwAAAGre4sWLZbfbNXr06IJtkZGRGjlypL777jvt2LGj1GOzs7PVrFmzgiBZkkJDQ9WiRQtF1dFaaTNmSCfmStS4cQFtCgAAACqIMLkGtGrlqZPMJHwAAAB12/r169W5c2c1adKkyPbevXtLkjZs2FDqseeff742btyoSZMmKTk5WSkpKfr73/+udevWacKECTXZ7IDIyZFee82s9+1rFgAAANQelLmoATabKXWxcSNhMgAAQF2Xnp7udfJF97bdu3eXeuykSZOUmpqqqVOn6sknn5QkNWjQQO+//76uu+66ch87IyND+/btK7ItOTlZkpl0MT8/3+fn4Q/z59uUmWk+gtx9t0P5+VaNPp7D4ZDT6ZTDPRQapaKvfEM/+YZ+8h195Rv6yXf0lW/oJ4+Kvl8kTK4hCQkmTKbMBQAAQN2Wk5NTpEyFW2RkZMHtpYmIiFDnzp01cOBA3XDDDXI6nZo5c6aGDBmizz77TH3LGbo7Y8YMTZ482ettWVlZyszMrMAzqVmWJb34YgtJUps2TvXvv1813TyHw6HDhw/LsiyFhvLRpyz0lW/oJ9/QT76jr3xDP/mOvvIN/eSRlZVVof3rd2/VIPckfFu3mjfONltg2wMAAICaERUVpdzc3BLbjx8/XnB7ae666y6tWbNGP/30k0JCTAW6G2+8UV27dtW99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Training complete!\n Best val accuracy : 0.9749\n Epochs trained : 17\n Outputs saved to : /kaggle/working/runs/classify\n══════════════════════════════════════════════════\n","output_type":"stream"}],"execution_count":26},{"id":"f2a3b4c5","cell_type":"code","source":"# ── Cell 11 — Per-class validation accuracy ──────────────────────────────────\n# Load best checkpoint and evaluate per class on the val set\n\nbest_ckpt = torch.load(OUTPUT_DIR / \"best.pt\", map_location=DEVICE)\nmodel.load_state_dict(best_ckpt[\"model_state\"])\nmodel.eval()\n\nclass_correct = [0] * NUM_CLASSES\nclass_total = [0] * NUM_CLASSES\n\nwith torch.no_grad():\n for imgs, labels in tqdm(val_loader, desc=\"Evaluating\", leave=False):\n imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)\n preds = model(imgs).argmax(1)\n for pred, label in zip(preds, labels):\n class_correct[label] += int(pred == label)\n class_total[label] += 1\n\nper_class_acc = [\n class_correct[i] / class_total[i] if class_total[i] > 0 else 0.0\n for i in range(NUM_CLASSES)\n]\n\n# Sort by accuracy\nsorted_idx = sorted(range(NUM_CLASSES), key=lambda i: per_class_acc[i])\nsorted_cls = [CLASS_NAMES[i] for i in sorted_idx]\nsorted_acc = [per_class_acc[i] for i in sorted_idx]\n\nfig, ax = plt.subplots(figsize=(10, max(4, NUM_CLASSES * 0.4)))\ncolors = [\"#d9534f\" if a < 0.5 else \"#f0ad4e\" if a < 0.8 else \"#5cb85c\" for a in sorted_acc]\nbars = ax.barh(sorted_cls, sorted_acc, color=colors)\nax.set_xlim(0, 1)\nax.axvline(0.8, color=\"gray\", linestyle=\"--\", linewidth=0.8, label=\"80% threshold\")\nax.set_xlabel(\"Validation accuracy\")\nax.set_title(\"Per-class validation accuracy (best checkpoint)\")\nax.legend()\nfor bar, acc in zip(bars, sorted_acc):\n ax.text(min(acc + 0.01, 0.96), bar.get_y() + bar.get_height()/2,\n f\"{acc:.2%}\", va=\"center\", fontsize=8)\nplt.tight_layout()\nplt.savefig(OUTPUT_DIR / \"per_class_accuracy.png\", dpi=150)\nplt.show()\n\n# Print struggling classes\nstruggling = [(CLASS_NAMES[i], per_class_acc[i]) for i in range(NUM_CLASSES)\n if per_class_acc[i] < 0.8]\nif struggling:\n print(\"\\n⚠ Classes below 80% accuracy (may need more data or augmentation):\")\n for cls, acc in sorted(struggling, key=lambda x: x[1]):\n print(f\" {cls:<30} {acc:.2%}\")\nelse:\n print(\"\\n✓ All classes above 80% — great training run!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T10:04:08.475205Z","iopub.execute_input":"2026-05-11T10:04:08.475979Z","iopub.status.idle":"2026-05-11T10:04:10.755032Z","shell.execute_reply.started":"2026-05-11T10:04:08.475948Z","shell.execute_reply":"2026-05-11T10:04:10.754244Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Evaluating: 0%| | 0/9 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AAAAAgGUIowAAAAAAAGAZwigAAAAAAABY5ie1Cwbicb3yYwAAAABJRU5ErkJggg==\n"},"metadata":{}},{"name":"stdout","text":"\n⚠ Classes below 80% accuracy (may need more data or augmentation):\n Aroma Gold 75.00%\n","output_type":"stream"}],"execution_count":27},{"id":"a3b4c5d6","cell_type":"code","source":"# ── Cell 12 — Confusion matrix ───────────────────────────────────────────────\nimport numpy as np\n\nconf_matrix = np.zeros((NUM_CLASSES, NUM_CLASSES), dtype=int)\n\nwith torch.no_grad():\n for imgs, labels in tqdm(val_loader, desc=\"Confusion matrix\", leave=False):\n imgs = imgs.to(DEVICE)\n preds = model(imgs).argmax(1).cpu()\n for pred, label in zip(preds, labels):\n conf_matrix[label][pred] += 1\n\n# Normalise rows to percentages\nrow_sums = conf_matrix.sum(axis=1, keepdims=True).clip(min=1)\nconf_norm = conf_matrix / row_sums\n\nfig_size = max(8, NUM_CLASSES * 0.6)\nfig, ax = plt.subplots(figsize=(fig_size, fig_size))\nim = ax.imshow(conf_norm, interpolation=\"nearest\", cmap=\"Blues\", vmin=0, vmax=1)\nplt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)\n\ntick_marks = np.arange(NUM_CLASSES)\nax.set_xticks(tick_marks); ax.set_xticklabels(CLASS_NAMES, rotation=45, ha=\"right\", fontsize=7)\nax.set_yticks(tick_marks); ax.set_yticklabels(CLASS_NAMES, fontsize=7)\nax.set_ylabel(\"True label\"); ax.set_xlabel(\"Predicted label\")\nax.set_title(\"Normalised Confusion Matrix (val set)\")\n\n# Annotate cells with percentage\nthresh = 0.5\nfor i in range(NUM_CLASSES):\n for j in range(NUM_CLASSES):\n ax.text(j, i, f\"{conf_norm[i,j]:.0%}\",\n ha=\"center\", va=\"center\",\n color=\"white\" if conf_norm[i,j] > thresh else \"black\",\n fontsize=max(5, 9 - NUM_CLASSES // 5))\n\nplt.tight_layout()\nplt.savefig(OUTPUT_DIR / \"confusion_matrix.png\", dpi=150)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T10:04:16.842005Z","iopub.execute_input":"2026-05-11T10:04:16.842290Z","iopub.status.idle":"2026-05-11T10:04:21.893165Z","shell.execute_reply.started":"2026-05-11T10:04:16.842264Z","shell.execute_reply":"2026-05-11T10:04:21.892146Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Confusion matrix: 0%| | 0/9 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\n"},"metadata":{}}],"execution_count":28},{"id":"b4c5d6e7","cell_type":"code","source":"# ── Cell 13 — List output files ──────────────────────────────────────────────\nprint(\"Files saved to /kaggle/working/runs/classify/\")\nprint()\nfor f in sorted(OUTPUT_DIR.rglob(\"*\")):\n size = f.stat().st_size / 1e6 if f.is_file() else 0\n label = f\" {f.name:<30} {size:.2f} MB\" if f.is_file() else f\" 📁 {f.name}/\"\n print(label)\n\nprint()\nprint(\"➜ Download best.pt + class_names.json and drop them next to 3_inference.py\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-11T10:05:28.943624Z","iopub.execute_input":"2026-05-11T10:05:28.944240Z","iopub.status.idle":"2026-05-11T10:05:28.951200Z","shell.execute_reply.started":"2026-05-11T10:05:28.944205Z","shell.execute_reply":"2026-05-11T10:05:28.950353Z"}},"outputs":[{"name":"stdout","text":"Files saved to /kaggle/working/runs/classify/\n\n best.pt 16.46 MB\n class_distribution.png 0.10 MB\n class_names.json 0.00 MB\n confusion_matrix.png 0.22 MB\n last.pt 16.46 MB\n per_class_accuracy.png 0.17 MB\n training_curves.png 0.05 MB\n\n➜ Download best.pt + class_names.json and drop them next to 3_inference.py\n","output_type":"stream"}],"execution_count":29}]} \ No newline at end of file diff --git a/2_train_classifier.py b/2_train_classifier.py new file mode 100644 index 0000000..d937060 --- /dev/null +++ b/2_train_classifier.py @@ -0,0 +1,359 @@ +""" +Script 2 — Classification Model Trainer +========================================= +Trains a lightweight EfficientNet-B0 (ImageNet pre-trained) on the +crop dataset produced by script 1. + +Expected dataset layout +----------------------- +crops_dataset/ + train/ + cola_can/ + pepsi_can/ + lays_chips/ + … + val/ + cola_can/ + … + test/ (optional) + … + +Outputs +------- +runs/classify/ + best.pt ← best checkpoint (val accuracy) + last.pt ← last checkpoint + class_names.json ← {idx: class_name} mapping used at inference + training_curves.png + +Usage +----- +python 2_train_classifier.py \ + --data_dir crops_dataset \ + --output_dir runs/classify \ + --model efficientnet_b0 \ + --epochs 50 \ + --batch_size 64 \ + --img_size 224 \ + --lr 1e-3 \ + --patience 10 \ + --workers 4 \ + --amp +""" + +import argparse +import json +import time +from pathlib import Path + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +from torchvision import datasets, models, transforms +from torchvision.models import ( + EfficientNet_B0_Weights, + EfficientNet_B2_Weights, + MobileNet_V3_Small_Weights, + ResNet50_1x64d_QuantizedWeights, +) +from torch.cuda.amp import GradScaler, autocast +import matplotlib.pyplot as plt +from tqdm import tqdm + + +# ─────────────────────────── args ──────────────────────────────────────────── + +SUPPORTED_MODELS = [ + "efficientnet_b0", + "efficientnet_b2", + "mobilenet_v3_small", + "resnet50", +] + + +def parse_args(): + p = argparse.ArgumentParser(description="Train a product classifier on crop dataset") + p.add_argument("--data_dir", required=True, help="Root of crops_dataset/ with train/ val/ subfolders") + p.add_argument("--output_dir", default="runs/classify", help="Where to save checkpoints & logs") + p.add_argument("--model", default="efficientnet_b0", choices=SUPPORTED_MODELS) + p.add_argument("--epochs", type=int, default=50) + p.add_argument("--batch_size", type=int, default=64) + p.add_argument("--img_size", type=int, default=224) + p.add_argument("--lr", type=float, default=1e-3) + p.add_argument("--weight_decay",type=float, default=1e-4) + p.add_argument("--patience", type=int, default=10, help="Early-stop patience (epochs)") + p.add_argument("--workers", type=int, default=4) + p.add_argument("--amp", action="store_true", help="Use Automatic Mixed Precision (CUDA only)") + p.add_argument("--freeze_backbone", action="store_true", + help="Freeze backbone, only train the classifier head") + p.add_argument("--seed", type=int, default=42) + return p.parse_args() + + +# ─────────────────────────── model factory ─────────────────────────────────── + +def build_model(name: str, num_classes: int, freeze_backbone: bool) -> nn.Module: + if name == "efficientnet_b0": + m = models.efficientnet_b0(weights=EfficientNet_B0_Weights.IMAGENET1K_V1) + if freeze_backbone: + for p in m.features.parameters(): + p.requires_grad = False + in_features = m.classifier[1].in_features + m.classifier[1] = nn.Linear(in_features, num_classes) + + elif name == "efficientnet_b2": + m = models.efficientnet_b2(weights=EfficientNet_B2_Weights.IMAGENET1K_V1) + if freeze_backbone: + for p in m.features.parameters(): + p.requires_grad = False + in_features = m.classifier[1].in_features + m.classifier[1] = nn.Linear(in_features, num_classes) + + elif name == "mobilenet_v3_small": + m = models.mobilenet_v3_small(weights=MobileNet_V3_Small_Weights.IMAGENET1K_V1) + if freeze_backbone: + for p in m.features.parameters(): + p.requires_grad = False + in_features = m.classifier[3].in_features + m.classifier[3] = nn.Linear(in_features, num_classes) + + elif name == "resnet50": + m = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2) + if freeze_backbone: + for name_, p in m.named_parameters(): + if "fc" not in name_: + p.requires_grad = False + m.fc = nn.Linear(m.fc.in_features, num_classes) + + else: + raise ValueError(f"Unknown model: {name}") + + return m + + +# ─────────────────────────── data ──────────────────────────────────────────── + +def build_loaders(data_dir: Path, img_size: int, batch_size: int, workers: int): + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + + train_tf = transforms.Compose([ + transforms.RandomResizedCrop(img_size, scale=(0.7, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.05), + transforms.RandomGrayscale(p=0.05), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + val_tf = transforms.Compose([ + transforms.Resize(int(img_size * 1.15)), + transforms.CenterCrop(img_size), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + + train_set = datasets.ImageFolder(str(data_dir / "train"), transform=train_tf) + val_set = datasets.ImageFolder(str(data_dir / "val"), transform=val_tf) + + # ── Weighted sampler — balances classes regardless of image count per class. + # Rare classes get oversampled, dominant ones get undersampled. + # If your dataset is already balanced this is a no-op in effect. + import numpy as np + from torch.utils.data import WeightedRandomSampler + targets = train_set.targets + class_counts = np.bincount(targets) + class_weights = 1.0 / class_counts + sample_weights = class_weights[targets] + sampler = WeightedRandomSampler( + weights = sample_weights, + num_samples = len(train_set), + replacement = True, + ) + + # ── Imbalance report ────────────────────────────────────────────────────── + ratio = class_counts.max() / max(class_counts.min(), 1) + if ratio > 3: + print(f" ⚠ Class imbalance detected — {ratio:.1f}x ratio " + f"(max {class_counts.max()} / min {class_counts.min()} images).") + print(" WeightedRandomSampler enabled to compensate.") + low = [train_set.classes[i] for i, c in enumerate(class_counts) if c < 20] + if low: + print(f" ⚠ Classes with <20 training images: {low}") + print(" Consider collecting more data or using --freeze_backbone.") + + train_loader = DataLoader(train_set, batch_size=batch_size, sampler=sampler, + num_workers=workers, pin_memory=True) + val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False, + num_workers=workers, pin_memory=True) + + return train_loader, val_loader, train_set.classes + + +# ─────────────────────────── training loop ─────────────────────────────────── + +def run_epoch(model, loader, criterion, optimizer, device, scaler, train: bool): + model.train(train) + total_loss, correct, total = 0.0, 0, 0 + + ctx = torch.enable_grad() if train else torch.no_grad() + with ctx: + for imgs, labels in tqdm(loader, desc="train" if train else "val ", leave=False): + imgs, labels = imgs.to(device), labels.to(device) + + with autocast(enabled=(scaler is not None)): + logits = model(imgs) + loss = criterion(logits, labels) + + if train: + optimizer.zero_grad(set_to_none=True) + if scaler: + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + optimizer.step() + + total_loss += loss.item() * imgs.size(0) + correct += (logits.argmax(1) == labels).sum().item() + total += imgs.size(0) + + return total_loss / total, correct / total + + +def plot_curves(history: dict, out_path: Path): + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4)) + epochs = range(1, len(history["train_loss"]) + 1) + + ax1.plot(epochs, history["train_loss"], label="Train") + ax1.plot(epochs, history["val_loss"], label="Val") + ax1.set_title("Loss"); ax1.set_xlabel("Epoch"); ax1.legend() + + ax2.plot(epochs, history["train_acc"], label="Train") + ax2.plot(epochs, history["val_acc"], label="Val") + ax2.set_title("Accuracy"); ax2.set_xlabel("Epoch"); ax2.legend() + + fig.tight_layout() + fig.savefig(str(out_path), dpi=150) + plt.close(fig) + print(f" Curves saved → {out_path}") + + +# ─────────────────────────── main ──────────────────────────────────────────── + +def main(): + args = parse_args() + torch.manual_seed(args.seed) + + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + data_dir = Path(args.data_dir) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Device: {device}") + + # Data + print("Loading dataset …") + train_loader, val_loader, class_names = build_loaders( + data_dir, args.img_size, args.batch_size, args.workers + ) + num_classes = len(class_names) + print(f" Classes ({num_classes}): {class_names}") + + # Save class names + class_map = {i: c for i, c in enumerate(class_names)} + with open(out_dir / "class_names.json", "w") as f: + json.dump(class_map, f, indent=2) + print(f" class_names.json → {out_dir / 'class_names.json'}") + + # Model + print(f"\nBuilding model: {args.model}") + model = build_model(args.model, num_classes, args.freeze_backbone).to(device) + trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + total = sum(p.numel() for p in model.parameters()) + print(f" Trainable params: {trainable:,} / {total:,}") + + # Optimiser & scheduler + criterion = nn.CrossEntropyLoss(label_smoothing=0.1) + optimizer = torch.optim.AdamW( + filter(lambda p: p.requires_grad, model.parameters()), + lr=args.lr, weight_decay=args.weight_decay + ) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs) + scaler = GradScaler() if (args.amp and device.type == "cuda") else None + + # Training + history = {"train_loss": [], "train_acc": [], "val_loss": [], "val_acc": []} + best_val_acc = 0.0 + patience_ctr = 0 + + print(f"\n{'─'*55}") + print(f" Starting training for {args.epochs} epochs") + print(f"{'─'*55}") + + for epoch in range(1, args.epochs + 1): + t0 = time.time() + + train_loss, train_acc = run_epoch(model, train_loader, criterion, optimizer, device, scaler, train=True) + val_loss, val_acc = run_epoch(model, val_loader, criterion, None, device, None, train=False) + + scheduler.step() + elapsed = time.time() - t0 + + history["train_loss"].append(train_loss) + history["train_acc"].append(train_acc) + history["val_loss"].append(val_loss) + history["val_acc"].append(val_acc) + + improved = val_acc > best_val_acc + tag = " ✓ best" if improved else "" + print( + f"Epoch {epoch:3d}/{args.epochs} | " + f"Train loss {train_loss:.4f} acc {train_acc:.4f} | " + f"Val loss {val_loss:.4f} acc {val_acc:.4f} | " + f"{elapsed:.1f}s{tag}" + ) + + # Save checkpoints + if improved: + best_val_acc = val_acc + patience_ctr = 0 + torch.save({ + "epoch": epoch, + "model_state": model.state_dict(), + "class_names": class_names, + "val_acc": val_acc, + "args": vars(args), + }, out_dir / "best.pt") + else: + patience_ctr += 1 + + torch.save({ + "epoch": epoch, + "model_state": model.state_dict(), + "class_names": class_names, + "val_acc": val_acc, + "args": vars(args), + }, out_dir / "last.pt") + + # Early stopping + if patience_ctr >= args.patience: + print(f"\n Early stopping triggered (no improvement for {args.patience} epochs)") + break + + plot_curves(history, out_dir / "training_curves.png") + + print("\n" + "═" * 55) + print(" Training complete!") + print(f" Best val accuracy : {best_val_acc:.4f}") + print(f" Checkpoints saved : {out_dir}") + print("═" * 55) + print("\n ➜ Next step:") + print(" python 3_inference.py \\") + print(f" --weights {out_dir / 'best.pt'} \\") + print(" --source /path/to/images \\") + print(" --detector_weights /path/to/best_yolo.pt") + + +if __name__ == "__main__": + main() diff --git a/3_inference.py b/3_inference.py new file mode 100644 index 0000000..5c13a1f --- /dev/null +++ b/3_inference.py @@ -0,0 +1,464 @@ +""" +Script 3 — End-to-end Inference (Detect → Classify) +===================================================== +1. Runs your YOLOv8n detector on each image / video frame. +2. Crops every detection. +3. Feeds each crop through the classification head. +4. Draws annotated bounding boxes with class names & confidence. + +Supported sources +----------------- + • A single image (--source image.jpg) + • A folder of images (--source /path/to/imgs) + • A video file (--source video.mp4) + • A webcam (--source 0) + +Usage +----- +python 3_inference.py \ + --detector_weights runs/yolo/best.pt \ + --classifier_weights runs/classify/best.pt \ + --source /path/to/images_or_video \ + --det_conf 0.3 \ + --cls_conf 0.5 \ + --img_size 640 \ + --padding 8 \ + --output_dir inference_results \ + --show \ + --save_crops +""" + +import argparse +import json +import time +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from torchvision import models, transforms +from ultralytics import YOLO +from tqdm import tqdm + + +# ─────────────────────────── args ──────────────────────────────────────────── + +def parse_args(): + p = argparse.ArgumentParser(description="YOLOv8 detect + classify inference") + p.add_argument("--detector_weights", default="detector/best.pt", help="YOLOv8n .pt (fine-tuned)") + p.add_argument("--classifier_weights", default="best.pt", help="Classifier best.pt from script 2") + p.add_argument("--source", required=True, help="Image / folder / video / webcam index") + p.add_argument("--det_conf", type=float, default=0.30, help="YOLO detection confidence") + p.add_argument("--det_iou", type=float, default=0.45, help="YOLO NMS IoU") + p.add_argument("--cls_conf", type=float, default=0.60, help="Min classifier confidence to label") + p.add_argument("--img_size", type=int, default=640, help="YOLO input size") + p.add_argument("--padding", type=int, default=8, help="Extra pixels around each crop") + p.add_argument("--cls_img_size",type=int, default=224, help="Classifier input size") + p.add_argument("--output_dir", default="inference_results") + p.add_argument("--show", action="store_true", help="Display results with cv2.imshow") + p.add_argument("--save_crops", action="store_true", help="Save individual crop images") + p.add_argument("--no_save", action="store_true", help="Do not save annotated images/video") + p.add_argument("--batch_cls", type=int, default=16, help="Classifier batch size per frame") + p.add_argument("--device", default="", help="cuda / cpu / mps (auto-detect if empty)") + return p.parse_args() + + +# ─────────────────────────── classifier wrapper ────────────────────────────── + +# ── Device-agnostic OOM detection ──────────────────────────────────────────── +# +# Each backend raises a different exception type for out-of-memory: +# +# CUDA → torch.cuda.OutOfMemoryError (subclass of RuntimeError) +# MPS → RuntimeError whose message contains "out of memory" +# CPU → MemoryError (Python built-in, raised by the OS allocator) +# +# We centralise the check here so nothing else needs to know the device type. + +def _is_oom(exc: BaseException) -> bool: + """Return True if *exc* represents an out-of-memory condition.""" + if isinstance(exc, MemoryError): # CPU / OS + return True + if torch.cuda.is_available() and isinstance(exc, torch.cuda.OutOfMemoryError): + return True # CUDA + if isinstance(exc, RuntimeError): # MPS + fallback CUDA + msg = str(exc).lower() + return "out of memory" in msg or "memory" in msg and "alloc" in msg + return False + + +def _free_device_cache(device: torch.device) -> None: + """Release any cached memory held by the current device allocator.""" + if device.type == "cuda": + torch.cuda.empty_cache() + elif device.type == "mps": + # torch.mps.empty_cache() was added in PyTorch 2.1 — guard for older versions + if hasattr(torch.mps, "empty_cache"): + torch.mps.empty_cache() + # CPU has no cache to release + + +def _sync_device(device: torch.device) -> None: + """Wait for all pending ops on *device* so OOM surfaces immediately.""" + if device.type == "cuda": + torch.cuda.synchronize() + elif device.type == "mps": + if hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + + +# ───────────────────────────────────────────────────────────────────────────── + +class ProductClassifier: + """Thin wrapper around the saved checkpoint from script 2.""" + + # Supported architectures — mirrors script 2 + _BUILDERS = { + "efficientnet_b0": (models.efficientnet_b0, lambda m, n: setattr(m.classifier, '1', torch.nn.Linear(m.classifier[1].in_features if hasattr(m.classifier[1], 'in_features') else 1280, n))), + "efficientnet_b2": (models.efficientnet_b2, lambda m, n: setattr(m.classifier, '1', torch.nn.Linear(m.classifier[1].in_features if hasattr(m.classifier[1], 'in_features') else 1408, n))), + "mobilenet_v3_small": (models.mobilenet_v3_small, lambda m, n: setattr(m.classifier, '3', torch.nn.Linear(m.classifier[3].in_features if hasattr(m.classifier[3], 'in_features') else 576, n))), + "resnet50": (models.resnet50, lambda m, n: setattr(m, 'fc', torch.nn.Linear(m.fc.in_features, n))), + } + + def __init__(self, weights_path: str, device: torch.device, img_size: int = 224): + ckpt = torch.load(weights_path, map_location=device) + self.class_names: list = ckpt["class_names"] + num_classes = len(self.class_names) + + model_name = ckpt.get("args", {}).get("model", "efficientnet_b0") + builder_fn, head_fn = self._BUILDERS[model_name] + self.model = builder_fn(weights=None) + head_fn(self.model, num_classes) + self.model.load_state_dict(ckpt["model_state"]) + self.model.to(device).eval() + self.device = device + + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + self.transform = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(int(img_size * 1.15)), + transforms.CenterCrop(img_size), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + + # Probe VRAM once at startup — stored and reused every frame. + # Can be overridden at runtime if OOM occurs (see predict_batch). + self._batch_size: int = self.calibrate_batch_size(img_size) + + def calibrate_batch_size(self, img_size: int, start: int = 2, max_size: int = 512) -> int: + """ + Probe the device at startup to find the largest batch that fits in memory. + + Works on CUDA, MPS, and CPU: + • CUDA / MPS — doubles the batch size until OOM, steps back with margin. + • CPU — RAM is virtually unlimited; returns a sensible fixed cap. + + Returns the largest safe batch size, always ≥ 1. + """ + if self.device.type == "cpu": + print(" [batch-probe] CPU detected — using fixed batch size 64") + return 64 + + print(f" [batch-probe] Probing {self.device.type.upper()} memory " + f"for optimal batch size (img={img_size}px) …", flush=True) + + dummy = torch.zeros(1, 3, img_size, img_size, device=self.device) + safe_size = 1 + probe_size = start + + with torch.no_grad(): + while probe_size <= max_size: + try: + batch = dummy.expand(probe_size, -1, -1, -1) + _ = self.model(batch) + + # Flush pending async ops so OOM surfaces here, not later + _sync_device(self.device) + + safe_size = probe_size + probe_size = probe_size * 2 + + except Exception as exc: + if _is_oom(exc): + break # safe_size is our answer + raise # unexpected error — propagate + + finally: + _free_device_cache(self.device) + + # 20 % headroom margin: real crops vary in size unlike the uniform dummy + safe_size = max(1, int(safe_size * 0.8)) + print(f" [batch-probe] Settled on batch_size = {safe_size} " + f"(probe ceiling was {probe_size // 2})") + return safe_size + + @torch.no_grad() + def predict_batch(self, crops_bgr: list, batch_size: int | None = None) -> list[tuple[str, float]]: + """ + Classifies an arbitrary list of crops in memory-safe chunks. + + crops_bgr : list of H×W×3 uint8 BGR numpy arrays + batch_size : chunk size to use. Pass None to use self._batch_size + (set by calibrate_batch_size at startup). + Returns : list of (class_name, confidence) — same order as input + """ + if not crops_bgr: + return [] + + # Resolve which batch size to use + chunk_size = batch_size if batch_size is not None else self._batch_size + + # Pre-process all crops on CPU (fast; no GPU memory involved yet) + tensors = [ + self.transform(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)) + for crop in crops_bgr + ] + + results: list[tuple[str, float]] = [] + + i = 0 + while i < len(tensors): + chunk = tensors[i : i + chunk_size] + batch = torch.stack(chunk).to(self.device) + + try: + logits = self.model(batch) + probs = F.softmax(logits, dim=1) + confs, idxs = probs.max(dim=1) + + for idx, conf in zip(idxs, confs): + results.append((self.class_names[idx.item()], conf.item())) + + i += chunk_size # advance only on success + + except Exception as exc: + if not _is_oom(exc): + raise # unexpected error — don't swallow it + + # ── Runtime OOM safety net ──────────────────────────────── + # The probe uses a uniform dummy; real crops vary and can + # occasionally exceed the probed limit. Halve and retry + # WITHOUT advancing i so the same chunk is retried. + _free_device_cache(self.device) + + new_size = max(1, chunk_size // 2) + print(f" [batch] Runtime OOM on {self.device.type.upper()} " + f"— halving chunk {chunk_size} → {new_size}") + chunk_size = new_size + self._batch_size = new_size # persist for future frames + + finally: + del batch # free GPU memory immediately after each chunk + + return results + + +# ─────────────────────────── drawing ───────────────────────────────────────── + +# One colour per class, generated on first use +_COLOUR_CACHE: dict[str, tuple] = {} + + +def class_colour(name: str) -> tuple: + if name not in _COLOUR_CACHE: + # OpenCV HSV: hue is 0–179 (NOT 0–360) — values above 179 overflow uint8 + h = hash(name) % 180 + col = cv2.cvtColor( + np.array([[[h, 220, 200]]], dtype=np.uint8), cv2.COLOR_HSV2BGR + )[0][0] + _COLOUR_CACHE[name] = tuple(int(c) for c in col) + return _COLOUR_CACHE[name] + + +def draw_result(frame, box_xyxy, label: str, conf: float, cls_conf_thresh: float): + x1, y1, x2, y2 = map(int, box_xyxy) + colour = class_colour(label) + cv2.rectangle(frame, (x1, y1), (x2, y2), colour, 2) + + if conf >= cls_conf_thresh: + text = f"{label} {conf:.2f}" + else: + text = "?" + + (tw, th), bl = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.55, 1) + cv2.rectangle(frame, (x1, y1 - th - bl - 4), (x1 + tw + 4, y1), colour, -1) + cv2.putText(frame, text, (x1 + 2, y1 - bl - 2), + cv2.FONT_HERSHEY_SIMPLEX, 0.55, (255, 255, 255), 1, cv2.LINE_AA) + return frame + + +# ─────────────────────────── source helpers ────────────────────────────────── + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".tiff"} + + +def is_image(path: Path) -> bool: + return path.suffix.lower() in IMAGE_EXTS + + +def is_video(path: Path) -> bool: + return path.suffix.lower() in {".mp4", ".avi", ".mov", ".mkv", ".webm"} + + +def iter_source(source: str): + """ + Yields (frame_bgr, frame_id, source_name) for every frame / image. + """ + try: + cam_idx = int(source) # webcam + cap = cv2.VideoCapture(cam_idx) + fid = 0 + while True: + ret, frame = cap.read() + if not ret: + break + yield frame, fid, f"webcam_{cam_idx}" + fid += 1 + cap.release() + return + except ValueError: + pass + + p = Path(source) + if p.is_file() and is_image(p): + img = cv2.imread(str(p)) + if img is not None: + yield img, 0, p.stem + elif p.is_file() and is_video(p): + cap = cv2.VideoCapture(str(p)) + fid = 0 + while True: + ret, frame = cap.read() + if not ret: + break + yield frame, fid, p.stem + fid += 1 + cap.release() + elif p.is_dir(): + img_paths = sorted(q for q in p.rglob("*") if is_image(q)) + for q in tqdm(img_paths, desc="Images"): + img = cv2.imread(str(q)) + if img is not None: + yield img, 0, q.stem + else: + raise FileNotFoundError(f"Cannot open source: {source}") + + +# ─────────────────────────── main ──────────────────────────────────────────── + +def main(): + args = parse_args() + + # Device + if args.device: + device = torch.device(args.device) + elif torch.cuda.is_available(): + device = torch.device("cuda") + elif torch.backends.mps.is_available(): + device = torch.device("mps") + else: + device = torch.device("cpu") + print(f"Device : {device}") + + # Load models + print(f"Loading detector : {args.detector_weights}") + detector = YOLO(args.detector_weights) + + print(f"Loading classifier: {args.classifier_weights}") + classifier = ProductClassifier(args.classifier_weights, device, args.cls_img_size) + print(f" Classes ({len(classifier.class_names)}): {classifier.class_names}") + + # Output directory + out_dir = Path(args.output_dir) + if not args.no_save: + out_dir.mkdir(parents=True, exist_ok=True) + crops_dir = out_dir / "crops" if args.save_crops else None + if crops_dir: + crops_dir.mkdir(parents=True, exist_ok=True) + + # Stats + stats = {"frames": 0, "detections": 0, "classified": 0, "fps": []} + + for frame, frame_id, stem in iter_source(args.source): + t0 = time.perf_counter() + + # ── Detection ────────────────────────────────────────────────────── + results = detector( + frame, + conf=args.det_conf, + iou=args.det_iou, + imgsz=args.img_size, + verbose=False, + )[0] + + h, w = frame.shape[:2] + crops, boxes = [], [] + + for box in results.boxes: + x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) + x1 = max(0, x1 - args.padding); y1 = max(0, y1 - args.padding) + x2 = min(w, x2 + args.padding); y2 = min(h, y2 + args.padding) + crop = frame[y1:y2, x1:x2] + if crop.size == 0: + continue + crops.append(crop) + boxes.append((x1, y1, x2, y2)) + + # ── Classification (dynamic chunked batches) ─────────────────────── + # Batch size is auto-calibrated to VRAM at startup via probe, + # and auto-reduces at runtime if OOM occurs (e.g. unusually dense frame). + predictions = classifier.predict_batch(crops) + + # ── Draw & annotate ─────────────────────────────────────────────── + annotated = frame.copy() + for (x1, y1, x2, y2), (cls_name, cls_conf) in zip(boxes, predictions): + draw_result(annotated, (x1, y1, x2, y2), cls_name, cls_conf, args.cls_conf) + if cls_conf >= args.cls_conf: + stats["classified"] += 1 + + # FPS overlay + elapsed = time.perf_counter() - t0 + fps = 1.0 / elapsed if elapsed > 0 else 0 + stats["fps"].append(fps) + cv2.putText(annotated, f"FPS: {fps:.1f} Det: {len(boxes)}", + (10, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) + + # ── Save / display ──────────────────────────────────────────────── + if not args.no_save: + out_path = out_dir / f"{stem}_frame{frame_id:06d}.jpg" + cv2.imwrite(str(out_path), annotated) + + if args.save_crops and crops_dir: + for i, (crop, (cls_name, cls_conf)) in enumerate(zip(crops, predictions)): + tag = cls_name if cls_conf >= args.cls_conf else "uncertain" + cv2.imwrite(str(crops_dir / f"{stem}_f{frame_id:06d}_c{i:04d}_{tag}.jpg"), crop) + + if args.show: + cv2.imshow("Detect + Classify", annotated) + key = cv2.waitKey(1) + if key in (ord("q"), 27): + break + + stats["frames"] += 1 + stats["detections"] += len(boxes) + + cv2.destroyAllWindows() + + # ── Summary ─────────────────────────────────────────────────────────── + avg_fps = np.mean(stats["fps"]) if stats["fps"] else 0 + print("\n" + "═" * 55) + print(" Inference summary") + print(f" Frames processed : {stats['frames']}") + print(f" Total detections : {stats['detections']}") + print(f" Classified (≥{args.cls_conf:.0%}): {stats['classified']}") + print(f" Avg FPS : {avg_fps:.1f}") + if not args.no_save: + print(f" Results saved to : {out_dir.resolve()}") + print("═" * 55) + + +if __name__ == "__main__": + main() diff --git a/README.md b/README.md new file mode 100644 index 0000000..8594601 --- /dev/null +++ b/README.md @@ -0,0 +1,628 @@ +# Product Detection & Classification Pipeline + +A two-stage computer vision pipeline that combines a fine-tuned **YOLOv8n** detector with a **EfficientNet** classifier to detect and identify products on retail shelves. + +``` +Shelf image → YOLOv8n (detect) → EfficientNet (classify) → labelled bounding boxes +``` + +--- + +## Table of Contents + +1. [Project Structure](#1-project-structure) +2. [Installation](#2-installation) +3. [Pipeline Overview](#3-pipeline-overview) +4. [Step-by-step Guide](#4-step-by-step-guide) + - [Step 1 — Generate crops](#step-1--generate-crops) + - [Step 2 — Auto-label & Review](#step-2--auto-label--review) + - [Step 3 — Split the dataset](#step-3--split-the-dataset) + - [Step 4 — Train the classifier](#step-4--train-the-classifier) + - [Step 5 — Run inference](#step-5--run-inference) +5. [API Server](#5-api-server) +6. [Script Reference](#6-script-reference) +7. [Troubleshooting](#7-troubleshooting) +8. [Tips & Best Practices](#8-tips--best-practices) + +--- + +## 1. Project Structure + +``` +project/ +│ +├── detector/ +│ └── best.pt ← your fine-tuned YOLOv8n weights +│ +├── runs/classify/ +│ ├── best.pt ← trained classifier weights (output of step 4) +│ ├── last.pt +│ ├── class_names.json ← {index: class_name} mapping +│ ├── training_curves.png +│ ├── per_class_accuracy.png +│ └── confusion_matrix.png +│ +├── data/ ← labelled crops (built incrementally) +│ ├── cola_can/ +│ ├── pepsi_can/ +│ ├── _rejected_/ ← crops you rejected during review +│ └── _unreviewed_/ ← crops you skipped during review +│ +├── crops_dataset/ ← final train/val/test split (input to training) +│ ├── train/ +│ ├── val/ +│ └── test/ +│ +├── 1_generate_crop_dataset.py ← detect & crop (no classifier needed) +├── auto_label.py ← detect + auto-classify + browser review UI +├── 1b_split_dataset.py ← stratified train/val/test splitter +├── 2_train_classifier.py ← train EfficientNet classifier +├── 3_inference.py ← end-to-end detect + classify on new images +├── api_server.py ← FastAPI polling server +└── train_classifier_kaggle.ipynb +``` + +--- + +## 2. Installation + +The project uses a **Conda environment** for reproducible dependency management. + +### Create the environment + +```bash +conda env create -f environment.yml +conda activate product-detection +``` + +### `environment.yml` + +```yaml +name: product-detection +channels: + - conda-forge +dependencies: + - python=3.10 + - numpy<2 + - pip + - pip: + - torch + - torchvision + - opencv-python + - ultralytics + - pillow==10.0.0 + - tqdm + - pyyaml + - simsimd + - albumentations + - faiss-cpu + - fastapi + - uvicorn[standard] + - flask +``` + +### GPU users + +`torch` and `torchvision` are installed as CPU builds by default via pip inside conda. To use a GPU, replace the `torch` and `torchvision` lines in the yml with the CUDA-enabled builds before creating the environment: + +```yaml + - torch==2.3.0+cu121 + - torchvision==0.18.0+cu121 + --extra-index-url https://download.pytorch.org/whl/cu121 +``` + +Or install them manually after environment creation: + +```bash +conda activate product-detection +pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 +``` + +### Update an existing environment + +If `environment.yml` changes, sync the existing environment without recreating it: + +```bash +conda env update -f environment.yml --prune +``` + +--- + +## 3. Pipeline Overview + +There are two ways to build your dataset depending on whether you already have a trained classifier: + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ PATH A — First time (no classifier yet) │ +│ │ +│ 1_generate_crop_dataset.py → label manually → │ +│ 1b_split_dataset.py → 2_train_classifier.py │ +└─────────────────────────────────────────────────────────────────┘ + +┌─────────────────────────────────────────────────────────────────┐ +│ PATH B — You already have a classifier (recommended after v1) │ +│ │ +│ auto_label.py → review in browser → │ +│ 1b_split_dataset.py → 2_train_classifier.py │ +└─────────────────────────────────────────────────────────────────┘ +``` + +Once you have a working classifier, always use **Path B**. It is faster, more accurate, and requires far less manual labelling work. + +--- + +## 4. Step-by-step Guide + +### Step 1 — Generate crops + +> Use this step if you are starting from scratch with **no classifier yet**. +> If you already have `runs/classify/best.pt`, skip to Step 2. + +Runs YOLOv8n on your shelf images and saves every detection as a cropped JPEG into `data/unknown/`. No classification happens here — you label manually afterwards. + +```bash +python 1_generate_crop_dataset.py \ + --source shelf_images/ \ + --weights detector/best.pt \ + --output_dir data \ + --conf 0.25 \ + --padding 10 \ + --min_size 32 +``` + +| Argument | Default | Description | +|---|---|---| +| `--source` | required | Image file, folder, or glob pattern | +| `--weights` | required | YOLOv8n `.pt` file | +| `--output_dir` | `data` | Root folder — crops go into `/unknown/` | +| `--conf` | `0.25` | Detection confidence threshold | +| `--iou` | `0.45` | NMS IoU threshold | +| `--padding` | `10` | Extra pixels added around each crop | +| `--min_size` | `32` | Discard crops smaller than this (px) | +| `--save_labels` | off | Save a `.txt` sidecar with YOLO class info | + +**After this step**, move crops from `data/unknown/` into named class folders: + +``` +data/ + unknown/ ← before + cola_can/ ← after (you create these) + pepsi_can/ + lays_chips/ +``` + +Use any file manager, or a tool like [Label Studio](https://labelstud.io/) for faster annotation. + +--- + +### Step 2 — Auto-label & Review + +> Use this step once you have a trained classifier. It replaces manual labelling almost entirely. + +Runs the full detect → crop → classify pipeline in one command, then opens a browser UI where you review, confirm, fix, or reject each prediction. + +```bash +python auto_label.py \ + --source shelf_images/ \ + --detector_weights detector/best.pt \ + --classifier_weights runs/classify/best.pt \ + --output_dir data \ + --cls_conf 0.50 +``` + +| Argument | Default | Description | +|---|---|---| +| `--source` | required | Image file, folder, or glob | +| `--detector_weights` | required | YOLOv8n `.pt` | +| `--classifier_weights` | required | Classifier `best.pt` | +| `--output_dir` | `data` | Where confirmed crops are written | +| `--det_conf` | `0.25` | Detection threshold | +| `--cls_conf` | `0.50` | Min classifier confidence to auto-label | +| `--port` | `5000` | Browser UI port | + +The browser opens automatically at `http://localhost:5000`. + +#### Review UI controls + +| Action | How | +|---|---| +| Open crop detail | Click a card | +| Confirm prediction | `C` key or **Confirm** button | +| Reject crop | `R` key or **Reject** button | +| Override the class | Pick from dropdown → **Apply override** | +| Navigate crops | `←` `→` arrow keys | +| Close modal | `Esc` | +| Multi-select | `Ctrl+click` or `Shift+click` | +| Bulk confirm/reject/reclassify | Select multiple → use bulk bar | +| Confirm all visible | **✓ Confirm all visible** button | +| Filter by class | Click a class in the left sidebar | +| Filter by status | Toggle buttons in toolbar | +| Filter by confidence | Drag the min-conf slider | + +#### Status meanings + +| Status | Meaning | +|---|---| +| `auto` | Classifier's prediction, not yet reviewed | +| `confirmed` | You agreed with the prediction | +| `reclassified` | You changed it to a different class | +| `rejected` | Not a useful crop (bad detection, not a product of interest) | + +#### Committing + +Click **Commit dataset →** to write all crops to `data/`: + +``` +data/ + cola_can/ ← confirmed + reclassified + pepsi_can/ + _rejected_/ ← rejected crops (kept for reference) + _unreviewed_/ ← anything still "auto" when you committed +``` + +> **Important:** Commit is always additive. It never deletes existing files in `data/`. +> Running `auto_label.py` on new shelf images simply grows the dataset. +> The only exception: if you run it twice on the **exact same source image**, crop files +> are overwritten (same content, so this is harmless). + +--- + +### Step 3 — Split the dataset + +Splits `data/` into `train/`, `val/`, and `test/` using a **stratified** strategy — every class gets the same ratio across all splits, so rare classes are not accidentally lost. + +```bash +python 1b_split_dataset.py \ + --data_dir data \ + --output_dir crops_dataset \ + --val_split 0.15 \ + --test_split 0.05 +``` + +| Argument | Default | Description | +|---|---|---| +| `--data_dir` | required | Labelled data root (subfolders = classes) | +| `--output_dir` | `crops_dataset` | Where to write train/val/test | +| `--val_split` | `0.15` | Fraction for validation | +| `--test_split` | `0.05` | Fraction for test (`0` to skip) | +| `--move` | off | Move files instead of copying (saves disk space) | +| `--min_per_class` | `2` | Warn if a class has fewer images than this after splitting | + +**Automatically skipped folders:** +- `unknown/`, `train/`, `val/`, `test/` +- Any folder whose name starts with `_` (e.g. `_rejected_`, `_unreviewed_`, `_ignore_`) + +The splitter prints a table so you can spot imbalanced or thin classes before training: + +``` +──────────────────────────────────────────────────────── + Class Train Val Test +──────────────────────────────────────────────────────── + cola_can 72 13 5 + pepsi_can 58 10 4 + lays_chips 12 2 ⚠ val<5 +──────────────────────────────────────────────────────── + TOTAL 142 25 9 +``` + +> **Recommended minimums:** ≥ 50 training images and ≥ 10 val images per class. +> Classes below these thresholds should get more data before training. + +--- + +### Step 4 — Train the classifier + +Trains an **EfficientNet-B0** (ImageNet pre-trained) on `crops_dataset/`. A `WeightedRandomSampler` is used automatically so class imbalance does not bias training. + +```bash +python 2_train_classifier.py \ + --data_dir crops_dataset \ + --model efficientnet_b0 \ + --epochs 50 \ + --batch_size 64 \ + --amp +``` + +| Argument | Default | Description | +|---|---|---| +| `--data_dir` | required | Root with `train/` and `val/` | +| `--output_dir` | `runs/classify` | Where checkpoints are saved | +| `--model` | `efficientnet_b0` | `efficientnet_b0`, `efficientnet_b2`, `mobilenet_v3_small`, `resnet50` | +| `--epochs` | `50` | Max training epochs | +| `--batch_size` | `64` | Batch size | +| `--img_size` | `224` | Input resolution | +| `--lr` | `1e-3` | Learning rate | +| `--patience` | `10` | Early stopping patience | +| `--amp` | off | Enable Automatic Mixed Precision (GPU only) | +| `--freeze_backbone` | off | Only train the head — recommended when you have fewer than ~50 images per class | + +**Outputs saved to `runs/classify/`:** + +| File | Description | +|---|---| +| `best.pt` | Best checkpoint by val accuracy | +| `last.pt` | Last epoch checkpoint | +| `class_names.json` | `{index: class_name}` used by inference and API | +| `training_curves.png` | Loss & accuracy plot | + +#### Option B — Kaggle notebook (recommended for free GPU access) + +`train_classifier_kaggle.ipynb` is a self-contained notebook version of the training script, designed to run on Kaggle's free T4 GPU with no local hardware required. + +**Setup steps:** + +1. Zip your `crops_dataset/` folder and upload it as a Kaggle Dataset: + - Go to [kaggle.com/datasets](https://www.kaggle.com/datasets) → **New Dataset** + - Upload the zip, give it a name (e.g. `my-product-crops`) + - Wait for processing to complete + +2. Create a new Kaggle Notebook: + - Go to [kaggle.com/code](https://www.kaggle.com/code) → **New Notebook** + - Upload `train_classifier_kaggle.ipynb` via **File → Import Notebook** + +3. Attach your dataset: + - In the right panel click **+ Add Data** + - Search for your dataset name and attach it + - It will appear at `/kaggle/input/my-product-crops/` + +4. Enable the GPU: + - Right panel → **Session options → Accelerator → GPU T4 x1** + +5. Edit **Cell 2 — Configuration** only: + ```python + DATA_DIR = Path("/kaggle/input/my-product-crops") # ← your dataset path + MODEL_NAME = "efficientnet_b0" + EPOCHS = 50 + BATCH_SIZE = 64 + ``` + +6. **Run All** (`Shift+Enter` through all cells, or **Run All** from the menu) + +**What the notebook does beyond the plain script:** + +| Cell | Extra feature | +|---|---| +| Cell 2b | Dataset diagnostics — prints per-class counts, flags classes with < 20 images, prints random-chance loss baseline | +| Cell 4 | Class distribution bar chart | +| Cell 5 | Augmented sample grid so you can visually verify your data loaded correctly | +| Cell 9 | Live training curves that update every epoch via `clear_output` | +| Cell 11 | Per-class validation accuracy with colour-coded bar chart (red < 50%, orange < 80%, green ≥ 80%) | +| Cell 12 | Normalised confusion matrix for the full val set | +| Cell 13 | Lists all output files with sizes | + +**Downloading outputs:** + +After training, download from `/kaggle/working/runs/classify/`: +- `best.pt` — copy to `runs/classify/best.pt` locally +- `class_names.json` — copy alongside `best.pt` + +These two files are all you need to run `3_inference.py` and `api_server.py`. + +#### Diagnosing a stuck val loss + +A val loss stuck above `ln(num_classes)` (e.g. > 3.13 for 23 classes) means the model is guessing randomly or confidently wrong. Common causes: + +| Symptom | Fix | +|---|---| +| Too few images per class (< 20) | Collect more data via `auto_label.py` on new images | +| Large imbalance between classes | `WeightedRandomSampler` is already enabled — check the imbalance report printed at startup | +| `_ignore_` class in dataset | **Remove it.** Use `--cls_conf` threshold for rejection instead | +| Val loss high but train loss low | Classic overfitting — add `--freeze_backbone` or collect more data | + +> **Do not use an `_ignore_` class.** Throwing unrelated products into one folder +> creates an incoherent class that harms the entire model. Use `--cls_conf` to +> reject low-confidence predictions at inference time instead. + +--- + +### Step 5 — Run inference + +Runs the full detect → classify pipeline on images, folders, videos, or webcam. Draws annotated bounding boxes and saves results. + +```bash +python 3_inference.py \ + --detector_weights detector/best.pt \ + --classifier_weights runs/classify/best.pt \ + --source shelf_images/ \ + --det_conf 0.30 \ + --cls_conf 0.65 \ + --output_dir inference_results \ + --show +``` + +| Argument | Default | Description | +|---|---|---| +| `--source` | required | Image, folder, video, or webcam index (`0`) | +| `--detector_weights` | required | YOLOv8n `.pt` | +| `--classifier_weights` | required | Classifier `best.pt` | +| `--det_conf` | `0.30` | Detection threshold | +| `--cls_conf` | `0.50` | Min classifier confidence to draw a label | +| `--det_iou` | `0.45` | NMS IoU threshold | +| `--padding` | `8` | Extra pixels added around each crop before classification | +| `--output_dir` | `inference_results` | Where annotated images/video are saved | +| `--show` | off | Display live with `cv2.imshow` | +| `--save_crops` | off | Also save individual crop images | +| `--no_save` | off | Skip saving annotated output | + +#### Dynamic batch sizing + +The classifier automatically probes available VRAM at startup and picks the largest safe batch size (with a 20% headroom margin). If an OOM occurs at runtime (e.g. an unusually dense frame with 260+ products), the batch is halved automatically and persisted for future frames. No manual tuning needed. + +--- + +## 5. API Server + +A REST API with a polling pattern for integration into other applications. + +```bash +pip install fastapi uvicorn +uvicorn api_server:app --host 0.0.0.0 --port 8000 --workers 1 +``` + +Configuration via environment variables: + +```bash +DETECTOR_WEIGHTS=detector/best.pt +CLASSIFIER_WEIGHTS=runs/classify/best.pt +DET_CONF=0.25 +CLS_CONF=0.50 +``` + +### Endpoints + +#### `POST /jobs` — Submit a job + +```json +{ + "image": "", + "interested_classes": ["cola_can", "pepsi_can"], + "cls_conf": 0.65 +} +``` + +`interested_classes` filters the results to only those classes. Send an empty list `[]` to return all detected products. + +Returns immediately: +```json +{ "job_id": "550e8400-e29b-41d4-a716-446655440000" } +``` + +#### `GET /jobs/{job_id}` — Poll for results + +Call every ~3 seconds until `status` is `"done"` or `"failed"`. + +```json +{ + "status": "processing", + "progress": 50, + "results": null +} +``` + +When done: +```json +{ + "status": "done", + "progress": 100, + "results": { + "annotated_image": "", + "counts": { + "cola_can": 3, + "pepsi_can": 1 + }, + "detections": [ + { "class": "cola_can", "confidence": 0.91, "bbox": [120, 45, 210, 180] }, + { "class": "cola_can", "confidence": 0.87, "bbox": [230, 48, 318, 182] }, + { "class": "pepsi_can", "confidence": 0.79, "bbox": [340, 51, 428, 185] } + ] + } +} +``` + +Progress stages: + +| Progress | Stage | +|---|---| +| 5 | Image decoded | +| 10 | Detection started | +| 30 | Detection done | +| 50 | Crops extracted | +| 75 | Classification done | +| 90 | Image annotated | +| 100 | Done | + +#### `GET /health` — Server health check + +```json +{ + "status": "ok", + "device": "cuda", + "classes": ["cola_can", "pepsi_can", "..."], + "active_jobs": 2 +} +``` + +#### `GET /classes` — List available classes + +```json +{ "classes": ["cola_can", "pepsi_can", "lays_chips", "..."] } +``` + +--- + +## 6. Script Reference + +| Script | Purpose | Inputs | Outputs | +|---|---|---|---| +| `1_generate_crop_dataset.py` | Detect & crop (no classifier) | shelf images | `data/unknown/*.jpg` | +| `auto_label.py` | Detect + auto-classify + browser review | shelf images + both models | `data//*.jpg` | +| `1b_split_dataset.py` | Stratified train/val/test split | `data/` | `crops_dataset/` | +| `2_train_classifier.py` | Train EfficientNet classifier | `crops_dataset/` | `runs/classify/best.pt` | +| `3_inference.py` | End-to-end detect + classify | images / video / webcam | annotated images/video | +| `api_server.py` | REST polling API | — | JSON + base64 annotated image | +| `train_classifier_kaggle.ipynb` | Kaggle training notebook | Kaggle dataset | `best.pt` + plots | + +--- + +## 7. Troubleshooting + +**`FileNotFoundError: No images found in: 01.jpg`** +You passed a file path that does not exist relative to your working directory. Use the full path or `cd` into the correct folder first. + +**`OverflowError: Python integer 276 out of bounds for uint8`** +This was a known bug (HSV hue > 179) — already fixed in the current `3_inference.py`. + +**Val loss stuck at ~5 during training** +See [Diagnosing a stuck val loss](#diagnosing-a-stuck-val-loss) above. + +**`CUDA out of memory` during inference** +The dynamic batch sizer handles this automatically by halving the batch and retrying. If it keeps happening, your crops may be very large — reduce `--padding` or `--img_size`. + +**`CUDA out of memory` during training** +Reduce `--batch_size`. Start at 32 and halve until it fits. + +**Browser does not open automatically after `auto_label.py`** +Open `http://localhost:5000` manually. + +**Classifier only predicts one class** +Almost always class imbalance. Check the imbalance report printed during `build_loaders`. The `WeightedRandomSampler` should compensate, but if one class has 10× more images, collect more data for the minority classes. + +--- + +## 8. Tips & Best Practices + +**Data collection** +- Aim for **≥ 50 training images per class** before expecting good results. +- Vary lighting, angles, partial occlusion, and zoom levels in your source images. +- Run `auto_label.py` on new batches of images regularly — the dataset grows incrementally and the model improves each iteration. + +**Labelling** +- Use the **confidence slider** in the review UI to start reviewing high-confidence crops first (`> 0.8`) — these are almost always correct and can be bulk-confirmed quickly. +- Filter the sidebar to one class at a time and use **Confirm all visible** — scanning one class at a time is much faster than random-order review. +- Never put unrecognised products into a catch-all class. Leave them in `_unreviewed_/` or reject them. Use `--cls_conf` to suppress uncertain predictions at inference time. + +**Training** +- Start with `--freeze_backbone` if you have fewer than 50 images per class. Once you have more data, retrain without it. +- Use the Kaggle notebook for free T4 GPU access — it includes a diagnostic cell that checks class counts and imbalance before training starts. +- `efficientnet_b0` is the best default — fast, accurate, small. Only upgrade to `efficientnet_b2` if B0 accuracy plateaus and you have plenty of data. + +**Iterative improvement loop** + +``` +Shoot new shelf photos + ↓ +auto_label.py (detect + auto-classify) + ↓ +Review UI (confirm / fix / reject) + ↓ +1b_split_dataset.py (re-split the grown dataset) + ↓ +2_train_classifier.py (retrain from scratch or from last.pt) + ↓ +3_inference.py / api_server.py (deploy updated model) + ↓ + repeat +``` + +Each iteration your classifier gets better, which means the auto-labelling in the next iteration needs less manual correction. diff --git a/api_server.py b/api_server.py new file mode 100644 index 0000000..e68eecb --- /dev/null +++ b/api_server.py @@ -0,0 +1,458 @@ +""" +API Server — Detect + Classify with Polling +============================================= +POST /jobs — submit an image + class filter, get back a job_id +GET /jobs/{job_id} — poll for status / progress / results + +Polling pattern +--------------- +1. Client POSTs image (base64) + interested_classes list +2. Server returns { "job_id": "" } immediately +3. Client polls GET /jobs/{job_id} every 3 s +4. Server returns: + { + "status": "queued" | "processing" | "done" | "failed", + "progress": 0‥100, + "results": { + "annotated_image": "", + "counts": { "cola_can": 3, "pepsi_can": 1, … }, + "detections": [ + { "class": "cola_can", "confidence": 0.91, + "bbox": [x1, y1, x2, y2] }, + … + ] + } // null while processing + } + +Run +--- +pip install fastapi uvicorn python-multipart + +uvicorn api_server:app --host 0.0.0.0 --port 8000 --workers 1 + +Environment variables (all optional) +------------------------------------- +DETECTOR_WEIGHTS path to YOLOv8n .pt (default: detector/best.pt) +CLASSIFIER_WEIGHTS path to classifier .pt (default: runs/classify/best.pt) +DET_CONF float (default: 0.25) +CLS_CONF float (default: 0.50) +DET_IOU float (default: 0.45) +PADDING int px (default: 10) +MAX_JOBS max jobs kept in memory (default: 200) +""" + +import base64 +import io +import json +import os +import uuid +from collections import OrderedDict +from concurrent.futures import ThreadPoolExecutor +from enum import Enum +from typing import Optional + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from fastapi import FastAPI, HTTPException, BackgroundTasks +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel, Field +from torchvision import models, transforms +from ultralytics import YOLO + +# ─────────────────────────── config ────────────────────────────────────────── + +DETECTOR_WEIGHTS = os.getenv("DETECTOR_WEIGHTS", "detector/best.pt") +CLASSIFIER_WEIGHTS = os.getenv("CLASSIFIER_WEIGHTS", "best.pt") +DET_CONF = float(os.getenv("DET_CONF", "0.25")) +CLS_CONF = float(os.getenv("CLS_CONF", "0.75")) +DET_IOU = float(os.getenv("DET_IOU", "0.45")) +PADDING = int(os.getenv("PADDING", "10")) +MAX_JOBS = int(os.getenv("MAX_JOBS", "200")) + +# ─────────────────────────── device ────────────────────────────────────────── + +def get_device() -> torch.device: + if torch.cuda.is_available(): + return torch.device("cuda") + if torch.backends.mps.is_available(): + return torch.device("mps") + return torch.device("cpu") + +DEVICE = get_device() + +# ─────────────────────────── OOM helpers (same as inference script) ────────── + +def _is_oom(exc: BaseException) -> bool: + if isinstance(exc, MemoryError): + return True + if torch.cuda.is_available() and isinstance(exc, torch.cuda.OutOfMemoryError): + return True + if isinstance(exc, RuntimeError): + msg = str(exc).lower() + return "out of memory" in msg or ("memory" in msg and "alloc" in msg) + return False + +def _free_device_cache(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.empty_cache() + elif device.type == "mps" and hasattr(torch.mps, "empty_cache"): + torch.mps.empty_cache() + +def _sync_device(device: torch.device) -> None: + if device.type == "cuda": + torch.cuda.synchronize() + elif device.type == "mps" and hasattr(torch.mps, "synchronize"): + torch.mps.synchronize() + +# ─────────────────────────── classifier ────────────────────────────────────── + +class ProductClassifier: + _BUILDERS = { + "efficientnet_b0": models.efficientnet_b0, + "efficientnet_b2": models.efficientnet_b2, + "mobilenet_v3_small": models.mobilenet_v3_small, + "resnet50": models.resnet50, + } + + def __init__(self, weights_path: str, device: torch.device, img_size: int = 224): + ckpt = torch.load(weights_path, map_location=device) + self.class_names: list[str] = ckpt["class_names"] + num_classes = len(self.class_names) + model_name = ckpt.get("args", {}).get("model", "efficientnet_b0") + + base = self._BUILDERS[model_name](weights=None) + # Attach correct head + if "efficientnet" in model_name: + base.classifier[1] = torch.nn.Linear(base.classifier[1].in_features, num_classes) + elif "mobilenet" in model_name: + base.classifier[3] = torch.nn.Linear(base.classifier[3].in_features, num_classes) + else: + base.fc = torch.nn.Linear(base.fc.in_features, num_classes) + + base.load_state_dict(ckpt["model_state"]) + self.model = base.to(device).eval() + self.device = device + + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + self.transform = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(int(img_size * 1.15)), + transforms.CenterCrop(img_size), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + self._batch_size: int = self._calibrate(img_size) + + def _calibrate(self, img_size: int) -> int: + if self.device.type == "cpu": + return 64 + dummy = torch.zeros(1, 3, img_size, img_size, device=self.device) + safe, probe = 1, 2 + with torch.no_grad(): + while probe <= 512: + try: + self.model(dummy.expand(probe, -1, -1, -1)) + _sync_device(self.device) + safe, probe = probe, probe * 2 + except Exception as exc: + if _is_oom(exc): + break + raise + finally: + _free_device_cache(self.device) + return max(1, int(safe * 0.8)) + + @torch.no_grad() + def predict_batch(self, crops_bgr: list[np.ndarray]) -> list[tuple[str, float]]: + if not crops_bgr: + return [] + tensors = [self.transform(cv2.cvtColor(c, cv2.COLOR_BGR2RGB)) for c in crops_bgr] + results: list[tuple[str, float]] = [] + chunk_size = self._batch_size + i = 0 + while i < len(tensors): + batch = torch.stack(tensors[i : i + chunk_size]).to(self.device) + try: + probs = F.softmax(self.model(batch), dim=1) + confs, idxs = probs.max(dim=1) + for idx, conf in zip(idxs, confs): + results.append((self.class_names[idx.item()], conf.item())) + i += chunk_size + except Exception as exc: + if not _is_oom(exc): + raise + _free_device_cache(self.device) + chunk_size = self._batch_size = max(1, chunk_size // 2) + finally: + del batch + return results + +# ─────────────────────────── model singletons ──────────────────────────────── + +print(f"[startup] Device: {DEVICE}") +print(f"[startup] Loading detector : {DETECTOR_WEIGHTS}") +DETECTOR = YOLO(DETECTOR_WEIGHTS) +print(f"[startup] Loading classifier: {CLASSIFIER_WEIGHTS}") +CLASSIFIER = ProductClassifier(CLASSIFIER_WEIGHTS, DEVICE) +print(f"[startup] Ready — classes: {CLASSIFIER.class_names}") + +# Thread pool: one worker keeps GPU access serialised; increase if CPU-only +EXECUTOR = ThreadPoolExecutor(max_workers=1) + +# ─────────────────────────── job store ─────────────────────────────────────── + +class Status(str, Enum): + queued = "queued" + processing = "processing" + done = "done" + failed = "failed" + +class Job: + def __init__(self, job_id: str, image_bytes: bytes, + interested_classes: list[str], cls_conf: float): + self.job_id = job_id + self.image_bytes = image_bytes + self.interested_classes = [c.lower() for c in interested_classes] + self.cls_conf = cls_conf + self.status = Status.queued + self.progress = 0 + self.results = None + self.error = None + +# OrderedDict so we can evict oldest jobs when MAX_JOBS is reached +JOB_STORE: OrderedDict[str, Job] = OrderedDict() + +def store_job(job: Job) -> None: + if len(JOB_STORE) >= MAX_JOBS: + JOB_STORE.popitem(last=False) # evict oldest + JOB_STORE[job.job_id] = job + +def get_job(job_id: str) -> Job: + job = JOB_STORE.get(job_id) + if job is None: + raise HTTPException(status_code=404, detail=f"Job '{job_id}' not found") + return job + +# ─────────────────────────── drawing ───────────────────────────────────────── + +_COLOUR_CACHE: dict[str, tuple] = {} + +def class_colour(name: str) -> tuple: + if name not in _COLOUR_CACHE: + h = hash(name) % 180 # OpenCV hue: 0–179 + col = cv2.cvtColor( + np.array([[[h, 220, 200]]], dtype=np.uint8), cv2.COLOR_HSV2BGR + )[0][0] + _COLOUR_CACHE[name] = tuple(int(c) for c in col) + return _COLOUR_CACHE[name] + +def draw_box(frame: np.ndarray, x1: int, y1: int, x2: int, y2: int, + label: str, conf: float) -> None: + colour = class_colour(label) + cv2.rectangle(frame, (x1, y1), (x2, y2), colour, 2) + text = f"{label} {conf:.2f}" + (tw, th), bl = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.55, 1) + cv2.rectangle(frame, (x1, y1 - th - bl - 4), (x1 + tw + 4, y1), colour, -1) + cv2.putText(frame, text, (x1 + 2, y1 - bl - 2), + cv2.FONT_HERSHEY_SIMPLEX, 0.55, (255, 255, 255), 1, cv2.LINE_AA) + +# ─────────────────────────── pipeline ──────────────────────────────────────── + +def _encode_image(frame: np.ndarray) -> str: + """Encode a BGR numpy array as a base64 JPEG string.""" + _, buf = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 90]) + return base64.b64encode(buf).decode("utf-8") + +def run_pipeline(job: Job) -> None: + """Blocking function — runs in the thread-pool executor.""" + try: + job.status = Status.processing + job.progress = 5 + + # ── Decode image ───────────────────────────────────────────────────── + arr = np.frombuffer(job.image_bytes, np.uint8) + frame = cv2.imdecode(arr, cv2.IMREAD_COLOR) + if frame is None: + raise ValueError("Could not decode image — unsupported format or corrupt data") + + job.progress = 10 + + # ── Detection ──────────────────────────────────────────────────────── + h, w = frame.shape[:2] + det_results = DETECTOR( + frame, conf=DET_CONF, iou=DET_IOU, verbose=False + )[0] + + job.progress = 30 + + # ── Crop each detection ─────────────────────────────────────────────── + crops, boxes = [], [] + for box in det_results.boxes: + x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) + x1 = max(0, x1 - PADDING); y1 = max(0, y1 - PADDING) + x2 = min(w, x2 + PADDING); y2 = min(h, y2 + PADDING) + crop = frame[y1:y2, x1:x2] + if crop.size == 0: + continue + crops.append(crop) + boxes.append((x1, y1, x2, y2)) + + job.progress = 50 + + # ── Classify ───────────────────────────────────────────────────────── + predictions = CLASSIFIER.predict_batch(crops) + + job.progress = 75 + + # ── Filter by interested_classes + cls_conf ─────────────────────────── + annotated = frame.copy() + counts: dict[str, int] = {} + detections: list[dict] = [] + + # If the user sends an empty list → show everything above cls_conf + filter_active = len(job.interested_classes) > 0 + + for (x1, y1, x2, y2), (cls_name, conf) in zip(boxes, predictions): + if conf < job.cls_conf: + continue + if filter_active and cls_name.lower() not in job.interested_classes: + continue + + draw_box(annotated, x1, y1, x2, y2, cls_name, conf) + counts[cls_name] = counts.get(cls_name, 0) + 1 + detections.append({ + "class": cls_name, + "confidence": round(conf, 4), + "bbox": [x1, y1, x2, y2], + }) + + job.progress = 90 + + # ── Encode output image ─────────────────────────────────────────────── + annotated_b64 = _encode_image(annotated) + + job.results = { + "annotated_image": annotated_b64, + "counts": counts, + "detections": detections, + } + job.status = Status.done + job.progress = 100 + + except Exception as exc: + job.status = Status.failed + job.error = str(exc) + raise + +# ─────────────────────────── schemas ───────────────────────────────────────── + +class SubmitRequest(BaseModel): + # Base64-encoded image (JPEG / PNG / BMP …) + image: str = Field(..., description="Base64-encoded image bytes") + # Classes to keep in results; empty list = keep all classes + interested_classes: list[str] = Field( + default=[], + description="Product class names to include. Leave empty to show all.", + examples=[["cola_can", "pepsi_can"]], + ) + # Per-request confidence override (optional) + cls_conf: Optional[float] = Field( + default=None, + ge=0.0, le=1.0, + description="Min classifier confidence (overrides server default)", + ) + +class SubmitResponse(BaseModel): + job_id: str + +class PollResponse(BaseModel): + status: Status + progress: int + results: Optional[dict] = None + error: Optional[str] = None + +# ─────────────────────────── app ───────────────────────────────────────────── + +app = FastAPI( + title="Product Detection API", + description="Detect and classify shelf products using YOLOv8 + EfficientNet", + version="1.0.0", +) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_methods=["*"], + allow_headers=["*"], +) + +# ── POST /jobs ──────────────────────────────────────────────────────────────── + +@app.post("/jobs", response_model=SubmitResponse, status_code=202) +async def submit_job(body: SubmitRequest, background_tasks: BackgroundTasks): + """ + Submit an image for detection + classification. + + Returns a job_id to poll with GET /jobs/{job_id}. + """ + # Decode base64 → raw bytes + try: + image_bytes = base64.b64decode(body.image) + except Exception: + raise HTTPException(status_code=422, detail="Invalid base64 in 'image' field") + + job_id = str(uuid.uuid4()) + job = Job( + job_id = job_id, + image_bytes = image_bytes, + interested_classes = body.interested_classes, + cls_conf = body.cls_conf if body.cls_conf is not None else CLS_CONF, + ) + store_job(job) + + # Submit to thread pool — does not block the event loop + EXECUTOR.submit(run_pipeline, job) + + return SubmitResponse(job_id=job_id) + + +# ── GET /jobs/{job_id} ──────────────────────────────────────────────────────── + +@app.get("/jobs/{job_id}", response_model=PollResponse) +async def poll_job(job_id: str): + """ + Poll the status of a submitted job. + + Call every ~3 seconds until status is 'done' or 'failed'. + """ + job = get_job(job_id) + return PollResponse( + status = job.status, + progress = job.progress, + results = job.results if job.status == Status.done else None, + error = job.error, + ) + + +# ── GET /health ─────────────────────────────────────────────────────────────── + +@app.get("/health") +async def health(): + return { + "status": "ok", + "device": str(DEVICE), + "classes": CLASSIFIER.class_names, + "active_jobs": sum(1 for j in JOB_STORE.values() + if j.status in (Status.queued, Status.processing)), + } + + +# ── GET /classes ────────────────────────────────────────────────────────────── + +@app.get("/classes") +async def list_classes(): + """Return all class names the classifier knows about.""" + return {"classes": CLASSIFIER.class_names} diff --git a/auto_label.py b/auto_label.py new file mode 100644 index 0000000..065bcba --- /dev/null +++ b/auto_label.py @@ -0,0 +1,986 @@ +""" +auto_label.py — Auto-classify crops + Web Review UI +===================================================== +Runs the full pipeline in one command: + + Step 1 Run YOLOv8n on your source images → crop every detection + Step 2 Run the classifier on every crop → propose a class label + Step 3 Open a local web UI so you review, confirm, fix, or reject + Step 4 On "Commit", write the final data/ folder ready for 1b_split_dataset.py + +Output layout (after commit) +----------------------------- +data/ + cola_can/ ← confirmed crops + pepsi_can/ + … + _rejected_/ ← crops you marked as reject + _unreviewed_/ ← anything you closed without reviewing + +Usage +----- +python auto_label.py \ + --source shelf_images/ \ + --detector_weights detector/best.pt \ + --classifier_weights runs/classify/best.pt \ + --output_dir data \ + --det_conf 0.25 \ + --cls_conf 0.50 \ + --port 5000 +""" + +import argparse +import base64 +import json +import os +import shutil +import sys +import threading +import time +import webbrowser +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from flask import Flask, jsonify, request, send_file +from torchvision import models, transforms +from tqdm import tqdm +from ultralytics import YOLO + +# ─────────────────────────── args ──────────────────────────────────────────── + +def parse_args(): + p = argparse.ArgumentParser(description="Auto-label crops and review in browser") + p.add_argument("--source", required=True) + p.add_argument("--detector_weights", default="detector/best.pt") + p.add_argument("--classifier_weights", default="best.pt") + p.add_argument("--output_dir", default="data") + p.add_argument("--staging_dir", default=".autolabel_staging", + help="Temp folder for staged crops before commit (hidden by default)") + p.add_argument("--det_conf", type=float, default=0.25) + p.add_argument("--det_iou", type=float, default=0.45) + p.add_argument("--cls_conf", type=float, default=0.75) + p.add_argument("--img_size", type=int, default=640) + p.add_argument("--padding", type=int, default=10) + p.add_argument("--min_size", type=int, default=32) + p.add_argument("--port", type=int, default=5000) + return p.parse_args() + +ARGS = parse_args() + +# ─────────────────────────── device / OOM ──────────────────────────────────── + +def get_device(): + if torch.cuda.is_available(): return torch.device("cuda") + if torch.backends.mps.is_available(): return torch.device("mps") + return torch.device("cpu") + +DEVICE = get_device() + +def _is_oom(exc): + if isinstance(exc, MemoryError): return True + if torch.cuda.is_available() and isinstance(exc, torch.cuda.OutOfMemoryError): return True + if isinstance(exc, RuntimeError): + m = str(exc).lower() + return "out of memory" in m or ("memory" in m and "alloc" in m) + return False + +def _free_cache(): + if DEVICE.type == "cuda": torch.cuda.empty_cache() + elif DEVICE.type == "mps" and hasattr(torch.mps, "empty_cache"): torch.mps.empty_cache() + +# ─────────────────────────── classifier ────────────────────────────────────── + +class ProductClassifier: + def __init__(self, weights_path, device, img_size=224): + ckpt = torch.load(weights_path, map_location=device) + self.class_names = ckpt["class_names"] + n = len(self.class_names) + name = ckpt.get("args", {}).get("model", "efficientnet_b0") + + m = { + "efficientnet_b0": models.efficientnet_b0, + "efficientnet_b2": models.efficientnet_b2, + "mobilenet_v3_small": models.mobilenet_v3_small, + "resnet50": models.resnet50, + }[name](weights=None) + + if "efficientnet" in name: m.classifier[1] = torch.nn.Linear(m.classifier[1].in_features, n) + elif "mobilenet" in name: m.classifier[3] = torch.nn.Linear(m.classifier[3].in_features, n) + else: m.fc = torch.nn.Linear(m.fc.in_features, n) + + m.load_state_dict(ckpt["model_state"]) + self.model = m.to(device).eval() + self.device = device + self.tf = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(int(img_size * 1.15)), + transforms.CenterCrop(img_size), + transforms.ToTensor(), + transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225]), + ]) + self._bs = self._probe(img_size) + + def _probe(self, sz): + if self.device.type == "cpu": return 64 + dummy = torch.zeros(1,3,sz,sz,device=self.device) + safe, probe = 1, 2 + with torch.no_grad(): + while probe <= 512: + try: + self.model(dummy.expand(probe,-1,-1,-1)) + safe, probe = probe, probe*2 + except Exception as e: + if _is_oom(e): break + raise + finally: _free_cache() + return max(1, int(safe*0.8)) + + @torch.no_grad() + def predict(self, crops_bgr): + if not crops_bgr: return [] + tensors = [self.tf(cv2.cvtColor(c, cv2.COLOR_BGR2RGB)) for c in crops_bgr] + results, i, bs = [], 0, self._bs + while i < len(tensors): + batch = torch.stack(tensors[i:i+bs]).to(self.device) + try: + probs = F.softmax(self.model(batch), dim=1) + confs, idxs = probs.max(dim=1) + # Return top-3 predictions per crop for the review UI + top3_probs, top3_idxs = torch.topk(probs, min(3, probs.shape[1]), dim=1) + for j in range(len(idxs)): + results.append({ + "class": self.class_names[idxs[j].item()], + "confidence": round(confs[j].item(), 4), + "top3": [ + {"class": self.class_names[top3_idxs[j][k].item()], + "conf": round(top3_probs[j][k].item(), 4)} + for k in range(top3_probs.shape[1]) + ] + }) + i += bs + except Exception as e: + if not _is_oom(e): raise + _free_cache() + bs = self._bs = max(1, bs // 2) + finally: + del batch + return results + +# ─────────────────────────── pipeline ──────────────────────────────────────── + +IMAGE_EXTS = {".jpg",".jpeg",".png",".bmp",".webp",".tiff"} + +def get_images(source): + p = Path(source) + if p.is_file() and p.suffix.lower() in IMAGE_EXTS: return [p] + if p.is_dir(): + imgs = sorted(f for f in p.rglob("*") if f.suffix.lower() in IMAGE_EXTS) + if not imgs: raise FileNotFoundError(f"No images in {p}") + return imgs + raise FileNotFoundError(f"Cannot open source: {source}") + +def img_to_b64(arr): + _, buf = cv2.imencode(".jpg", arr, [cv2.IMWRITE_JPEG_QUALITY, 88]) + return base64.b64encode(buf).decode() + +def run_pipeline(detector, classifier, args): + """ + Detect → crop → classify → stage. + Returns list of crop metadata dicts that the review UI reads. + """ + staging = Path(args.staging_dir) + if staging.exists(): shutil.rmtree(staging) + staging.mkdir(parents=True) + + image_paths = get_images(args.source) + print(f"\n[pipeline] {len(image_paths)} source image(s) found") + + crops_meta = [] # master list for the review UI + total_det = total_skip = 0 + + for img_path in tqdm(image_paths, desc="Detecting"): + frame = cv2.imread(str(img_path)) + if frame is None: + print(f" ⚠ Cannot read {img_path.name}") + continue + + h, w = frame.shape[:2] + results = detector(frame, conf=args.det_conf, iou=args.det_iou, + imgsz=args.img_size, verbose=False)[0] + + boxes, crops_bgr = [], [] + for box in results.boxes: + x1,y1,x2,y2 = map(int, box.xyxy[0].tolist()) + x1=max(0,x1-args.padding); y1=max(0,y1-args.padding) + x2=min(w,x2+args.padding); y2=min(h,y2+args.padding) + crop = frame[y1:y2, x1:x2] + if crop.size==0 or (x2-x1)/ + cls_dir = staging / cls_name + cls_dir.mkdir(exist_ok=True) + crop_path = cls_dir / f"{crop_id}.jpg" + cv2.imwrite(str(crop_path), crop) + + crops_meta.append({ + "id": crop_id, + "file": str(crop_path), + "source_image": img_path.name, + "pred_class": cls_name, + "confidence": conf, + "top3": pred["top3"], + "bbox": list(box), + # review fields (mutated by the UI) + "label": cls_name, # user's chosen label + "status": "auto", # "auto" | "confirmed" | "rejected" | "reclassified" + }) + total_det += 1 + + print(f"[pipeline] {total_det} crops staged | {total_skip} skipped") + return crops_meta + +# ─────────────────────────── Flask app ─────────────────────────────────────── + +flask_app = Flask(__name__) +STATE = { + "crops": [], # list of crop metadata dicts + "classes": [], # classifier class names + "done": False, # set to True when user commits +} + +@flask_app.route("/") +def index(): + return HTML_UI + +@flask_app.route("/api/crops") +def api_crops(): + return jsonify(STATE["crops"]) + +@flask_app.route("/api/classes") +def api_classes(): + return jsonify(STATE["classes"]) + +@flask_app.route("/api/crop_image/") +def crop_image(crop_id): + crop = next((c for c in STATE["crops"] if c["id"] == crop_id), None) + if not crop: + return "not found", 404 + return send_file(crop["file"], mimetype="image/jpeg") + +@flask_app.route("/api/update", methods=["POST"]) +def api_update(): + """Receive bulk label updates from the UI.""" + updates = request.json # list of {id, label, status} + lookup = {c["id"]: c for c in STATE["crops"]} + for u in updates: + if u["id"] in lookup: + lookup[u["id"]]["label"] = u["label"] + lookup[u["id"]]["status"] = u["status"] + return jsonify({"ok": True}) + +@flask_app.route("/api/commit", methods=["POST"]) +def api_commit(): + """Move staged crops to final output_dir based on user decisions.""" + out_root = Path(ARGS.output_dir) + + moved = rejected = unreviewed = 0 + for crop in STATE["crops"]: + src = Path(crop["file"]) + if not src.exists(): + continue + + status = crop["status"] + label = crop["label"] + + if status == "rejected": + dst_dir = out_root / "_rejected_" + elif status == "unreviewed" or status == "auto": + dst_dir = out_root / "_unreviewed_" + else: + dst_dir = out_root / label + + dst_dir.mkdir(parents=True, exist_ok=True) + shutil.copy2(str(src), str(dst_dir / src.name)) + + if status == "rejected": rejected += 1 + elif status in ("auto","unreviewed"): unreviewed += 1 + else: moved += 1 + + summary = { + "committed": moved, + "rejected": rejected, + "unreviewed": unreviewed, + "output_dir": str(out_root.resolve()), + } + STATE["done"] = True + return jsonify(summary) + +@flask_app.route("/api/stats") +def api_stats(): + crops = STATE["crops"] + by_class = {} + for c in crops: + by_class.setdefault(c["pred_class"], 0) + by_class[c["pred_class"]] += 1 + statuses = {} + for c in crops: + statuses[c["status"]] = statuses.get(c["status"], 0) + 1 + return jsonify({"by_class": by_class, "by_status": statuses, "total": len(crops)}) + +# ─────────────────────────── embedded UI ───────────────────────────────────── + +HTML_UI = r""" + + + + +Auto-Label Review + + + + + +
+ +

Auto-Label Review

+

Loading crops…

+
+ + +
+

⬡ AutoLabel

+
+
+ + +
+
+ +
+ + + + + +
+ +
+ Status: + + + + + +
+ Min conf: + 0.00 +
+
+ + +
+ 0 selected + + + + +
+ +
+
+
+ + + + + +
+
+ +

Reclassify crops

+
All selected crops will be moved to this class.
+ +
+ + +
+
+
+ + +
+
+ +

Commit dataset

+
+
+
+ + + +""" + +# ─────────────────────────── entry point ───────────────────────────────────── + +def main(): + args = ARGS + + print(f"[startup] Device : {DEVICE}") + print(f"[startup] Detector : {args.detector_weights}") + print(f"[startup] Classifier : {args.classifier_weights}") + + detector = YOLO(args.detector_weights) + classifier = ProductClassifier(args.classifier_weights, DEVICE) + + print("\n[step 1/2] Running detection + auto-classification …") + crops = run_pipeline(detector, classifier, args) + + if not crops: + print("⚠ No crops were generated. Check --source and --det_conf.") + sys.exit(1) + + STATE["crops"] = crops + STATE["classes"] = classifier.class_names + + print(f"\n[step 2/2] Launching review UI on http://localhost:{args.port}") + print(" Keyboard shortcuts inside a crop:") + print(" C → confirm") + print(" R → reject") + print(" ← → → previous / next crop") + print(" Esc → close modal") + print(" Ctrl/Shift+click → multi-select for bulk actions") + print("\n Close the browser tab or press Ctrl+C to stop.\n") + + # Open browser after a short delay so Flask is ready + def _open(): + time.sleep(1.2) + webbrowser.open(f"http://localhost:{args.port}") + threading.Thread(target=_open, daemon=True).start() + + # Run Flask (single-threaded to keep GPU access safe) + flask_app.run(host="0.0.0.0", port=args.port, threaded=False, use_reloader=False) + + +if __name__ == "__main__": + main() diff --git a/best.pt b/best.pt new file mode 100644 index 0000000..1a37393 Binary files /dev/null and b/best.pt differ diff --git a/class_names.json b/class_names.json new file mode 100644 index 0000000..4296179 --- /dev/null +++ b/class_names.json @@ -0,0 +1,32 @@ +{ + "0": "1001", + "1": "1001 Gold", + "2": "A100", + "3": "Aroma", + "4": "Aroma Espresso", + "5": "Aroma Familial", + "6": "Aroma Gold", + "7": "Bonal", + "8": "Bonal Excellence", + "9": "Cafe D Or Caps", + "10": "Canastel", + "11": "Caps", + "12": "CityOne Instant Coffee", + "13": "Dozia", + "14": "El Kabir", + "15": "Facto", + "16": "Facto no", + "17": "Famico", + "18": "Famico Caps", + "19": "Famico Exclusive", + "20": "Molino", + "21": "Mundo", + "22": "Mundo Caps", + "23": "Nizier", + "24": "Nouara", + "25": "Oscar", + "26": "Primo", + "27": "Ricamar Thon", + "28": "Siglo", + "29": "Skor" +} \ No newline at end of file diff --git a/data/1001 Gold/WhatsApp Image 2026-04-23 at 11.34.29 (1)_crop0002.jpg b/data/1001 Gold/WhatsApp Image 2026-04-23 at 11.34.29 (1)_crop0002.jpg new file mode 100644 index 0000000..d5cd0f1 Binary files /dev/null and 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