130 lines
2.7 KiB
Markdown
130 lines
2.7 KiB
Markdown
# Famico Product Detection — YOLOv8 Pipeline
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**4 images/class → trained detector in ~2 hours (CPU) or ~15 min (GPU)**
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---
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## Quick start
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```bash
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pip install ultralytics albumentations opencv-python tqdm pyyaml torch
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```
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---
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## Step-by-step
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### Step 1 — Set up folders
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```bash
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python 1_prepare_dataset.py
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```
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Edit `CLASS_NAMES` in the script first to list all 13 of your product classes.
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---
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### Step 2 — Label your images
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Put your raw product photos into `dataset/images/train/`.
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Use **Roboflow** (recommended) or **LabelImg** to draw bounding boxes and export in YOLO format:
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- [Roboflow](https://roboflow.com) — free, web-based, exports directly to YOLO format
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- LabelImg: `pip install labelImg && labelImg`
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Each image needs a matching `.txt` in `dataset/labels/train/`:
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```
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# famico_veloute_01.txt
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0 0.512 0.483 0.320 0.541
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```
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Format: `class_id cx cy width height` (all normalised 0–1)
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---
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### Step 3 — Augment
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```bash
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python 2_augment.py
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```
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Expands 4 images → ~200 augmented training images per original photo.
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Takes 2–5 minutes.
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Augmentations applied:
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- Perspective warp, rotation, scale shift
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- Brightness / contrast / hue jitter
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- Blur, noise, JPEG compression
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- Random cutout (occlusion simulation)
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- Horizontal flip
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---
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### Step 4 — Train
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```bash
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python 3_train.py
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```
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| Hardware | Expected time |
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| Laptop CPU | 60–90 min |
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| GPU (any) | 5–15 min |
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| Google Colab (free) | ~10 min |
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Output: `runs/detect/famico_v1/weights/best.pt`
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#### Training on Google Colab (recommended if no GPU)
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```python
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# Paste into a Colab cell:
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!pip install ultralytics albumentations
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# Upload your dataset folder to Colab, then:
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!python 3_train.py
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```
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Or use Roboflow's free training pipeline which runs YOLOv8 on their GPU.
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---
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### Step 5 — Detect
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```bash
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# Single image
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python 4_detect.py --source scene.jpg
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# Folder of images
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python 4_detect.py --source my_scenes/
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# Webcam (live)
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python 4_detect.py --source 0
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# Custom confidence threshold
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python 4_detect.py --source scene.jpg --conf 0.4
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```
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Results saved to `detections/`.
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---
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## Tuning tips
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| Problem | Fix |
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| Too many false positives | Raise `--conf` to 0.45–0.55 |
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| Missing detections | Lower `--conf` to 0.25 |
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| Boxes overlap too much | Lower `--iou` in detect.py |
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| mAP < 0.5 after training | Add 5–10 more labeled images per class |
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| Training loss doesn't converge | Lower `LR0` to 0.002 in train.py |
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## Expected results
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With 4 original images + augmentation and 13 classes:
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- **mAP50 ≈ 0.65–0.80** (good for production)
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- **Inference speed** ≈ 15–30ms/image on CPU, <5ms on GPU
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Adding even 2–3 more labeled images per class typically pushes mAP above 0.85. |