add tryin.py

.gitignore

@@ -1,3 +1,4 @@
 # Dataset
 dataset/
 dataset.yaml
+dataset.yml

enviroment.yml

@@ -9,10 +9,9 @@ dependencies:
   - tqdm
   - pyyaml
   - simsimd
-  #- torch
-  #- numpy
-  #- opencv-contrib-python
-  #- scikit-learn # @Todo: Remove it later if we don't need it.
+  - torch
+  - torchvision
+  - numpy
   - pip
   - pip:
       - fastapi

runs/detect/runs/detect/coffee_v1/args.yaml

@@ -0,0 +1,110 @@
+task: detect
+mode: train
+model: yolov8n.pt
+data: dataset.yml
+epochs: 150
+time: null
+patience: 30
+batch: 8
+imgsz: 640
+save: true
+save_period: 10
+cache: false
+device: cpu
+workers: 8
+project: runs/detect
+name: coffee_v1
+exist_ok: true
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 42
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: 0.25
+iou: 0.5
+max_det: 300
+half: false
+dnn: false
+plots: true
+end2end: null
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.005
+lrf: 0.01
+momentum: 0.938
+weight_decay: 0.0005
+warmup_epochs: 5
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+cls_pw: 0.0
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+rle: 1.0
+angle: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 5.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.1
+cutmix: 0.0
+copy_paste: 0.1
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: C:\Users\TRIZ\clones\YOLO-Coffee-Object-Detection\runs\detect\runs\detect\coffee_v1

runs/detect/runs/detect/coffee_v1/results.csv

@@ -0,0 +1,2 @@
+epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
+1,3381.49,1.0705,3.09597,1.50767,0.60417,0.5,0.4975,0.31896,1.31385,1.90113,1.49596,0.000117376,0.000117376,0.000117376

train.py

@@ -0,0 +1,151 @@
+"""
+STEP 3 - train.py
+================
+
+Fine-tunes YOLOv8-nano on your augmented dataset.
+Pre-trained COCO weights give you a huve head-start - the backbone
+already knows edges, textures, and shapes; it only needs to learn
+what Famico (and other products) look like.
+
+Install:
+    pip install ultralytics
+
+Usage:
+    python train.py
+
+Outputs:
+    runs/detect/coffee_v1/weights/best.pt <- use this for detection
+"""
+
+import os
+import torch
+from pathlib import Path
+from ultralytics import YOLO
+
+# ── Configuration ───────────────────────────────────────────────────
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+DATASET_YAML = "dataset.yml" # Generated by prepare_dataset.py
+MODEL        = "yolov8n.pt"  # nano - fast, good for few-shot
+                             # alternativse to try: yolov8s.pt (better acc)
+                             #                      yolov8m.pt (even better, needs GPU)
+
+PROJECT      = "runs/detect"
+RUN_NAME     = "coffee_v1"
+IMG_SIZE     = 640
+
+
+# ── Training hyperparameters ───────────────────────────────────────────────────
+#
+# Tuned for: small dataset (few-shot), packaged goods, retail shelf.
+#
+EPOCHS        = 150
+PATIENCE      = 30        # Early Stopping - stops if no improvement
+BATCH         = 8         # Lower if you get OOM on CPU/small GPU.
+LR0           = 0.005     # Initial LR (Learing Rate) - lower than default (0.01)
+
+LRF           = 0.01      # final LR = LR0 * LRF
+MOMENTUM      = 0.938
+WEIGHT_DECAY  = 0.0005
+WARMUP_EPOCHS = 5         # Longer warmup stabilises few-shot training.
+IOU_THRESH    = 0.5       # IoU threshold for NMS.
+CONF_THRESH   = 0.25      # confidence threshold at val time.
+
+
+# ── YOLOv8 built-in augmentation (on top of our albumentatios pass) ────────────
+#
+# These run during tarining batches - complementary to augment.py.
+# Mosaic is the single most impactful augmentation for small datasets.
+#
+MOSAIC     = 1.0    # mosaic probability
+MIXUP      = 0.1    # mixup probability
+COPY_PASTE = 0.1
+DEGREES    = 5.0    # degrees ±5°
+TRANSLATE  = 0.1
+SCALE      = 0.5
+FLIPUD     = 0.0
+FLIPLR     = 0.5
+HSV_H      = 0.015
+HSV_S      = 0.7
+HSV_V      = 0.4
+
+def check_enviroment():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if device == "cpu":
+        print("[INFO] No GPU detected - trainning on CPU")
+        print("       Expected time: ~60-90min for 150 epochs on a typical laptop.")
+        print("       Tips: use Google Colab or Kaggle they got free GPU, if training is too slow.\n")
+    else:
+        gpu= torch.cuda.get_device_name(0)
+        print(f"[INFO] GPU detected: {gpu}")
+        print("        Expected time: ~5-15min.\n")
+    return device
+
+def train():
+    device = check_enviroment()
+
+    if not Path(DATASET_YAML).exists():
+        print(f"[ERROR] {DATASET_YAML} not found.")
+        print("         Run prepare_dataset.py first.")
+        return
+
+    model = YOLO(MODEL)
+    print(f"Starting training: {EPOCHS} epochs, img size {IMG_SIZE}")
+    print(f"Dataset: {DATASET_YAML}")
+    print(f"Output:  {PROJECT}/{RUN_NAME}/weights/best.pt\n")
+
+    results = model.train(
+            data          = DATASET_YAML,
+            epochs        = EPOCHS,
+            patience      = PATIENCE,
+            imgsz         = IMG_SIZE,
+            batch         = BATCH,
+            device        = device,
+            project       = PROJECT,
+            name          = RUN_NAME,
+            exist_ok      = True,
+
+            lr0           = LR0,
+            lrf           = LRF,
+            momentum      = MOMENTUM,
+            weight_decay  = WEIGHT_DECAY,
+            warmup_epochs = WARMUP_EPOCHS,
+
+            iou           = IOU_THRESH,
+            conf          = CONF_THRESH,
+
+            # Built-in augmentation
+            mosaic        = MOSAIC,
+            mixup         = MIXUP,
+            copy_paste    = COPY_PASTE,
+            degrees       = DEGREES,
+            translate     = TRANSLATE,
+            scale         = SCALE,
+            flipud        = FLIPUD,
+            fliplr        = FLIPLR,
+            hsv_h         = HSV_H,
+            hsv_s         = HSV_S,
+            hsv_v         = HSV_V,
+
+            save          = True,
+            save_period   = 10,    # Checkpoint every 10 epochs
+            plots         = True,  # Saves training curvse as PNGs
+            verbose       = True,
+            seed          = 42,
+    )
+
+    best_weights = Path(PROJECT) / RUN_NAME / "weights" / "best.pt"
+    print("\n" + "-" * 60)
+    print(f"Training complete.")
+    print(f"Best weights saved -> {best_weights}")
+    print(f"mAP50: {results.results_dict.get('metrics/mAP50(B)', 'N/A'):.3f}")
+    print(f"mAP50-95: {results.results_dict.get('metrics/mAP50-95(B)', 'N/A'):.3f}")
+    print("-" * 60)
+    print("\nRun next: python detect.py --source scene.jpg")
+
+    return best_weights
+
+if __name__ == "__main__":
+    train()
+
+