vishesh1234/swach_v1

SWACH_V1

1. Model Specifications

• Architecture: YOLOv8s (small variant)
• Parameters: 11,137,922
• Gradients: 11,137,906
• Computational Load: 28.7 GFLOPs
• Input Size: 640×640 pixels
• Classes: 6 garbage categories (overridden from default COCO 80-class setup)

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2. Training Configuration

• Epochs: 100
• Batch Size: 16
• Device: NVIDIA Tesla T4 GPU (CUDA)
• Optimizer: AdamW
  • Learning Rate (lr0): 0.001
  • Momentum: 0.9
  • Weight Decay: 0.0005
• Data Augmentation:
  • Albumentations: Blur, MedianBlur, CLAHE, Grayscale conversion
  • Mosaic Augmentation: Enabled until epoch 90 (improves small-object detection)
• Mixed Precision: AMP (Automatic Mixed Precision) ✅
• Loss Functions:
  • box_loss: Bounding box regression
  • cls_loss: Class prediction
  • dfl_loss: Distribution Focal Loss (for localization accuracy)

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3. Dataset

• Training Images: 1,077 (+15 background)
• Validation Images: 157 (+1 background)
• Instances: 587 annotated objects
• Class Distribution:
  • garbage, sampah-detection, trash, and 3 additional classes

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4. Performance Metrics

Best Model Validation Results (Saved as swach_v1.pt):
metrics:

Metric	Value
mAP50	0.324
mAP50-95	0.197
Precision (P)	0.438
Recall (R)	0.356

Per-Class Results:

Class	Precision	Recall	mAP50
garbage	0.155	0.597	0.173
sampah-detection	0.447	0.676	0.599
trash	0.459	0.500	0.662
Class 0 (Unknown)	0.128	0.005	0.187

Key Insight: The model excels at detecting sampah-detection and trash (mAP50 > 0.59) but struggles with class 0 (low recall).

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5. Training Trajectory

• Total Time: 0.69 hours (~41 minutes)
• Loss Reduction:
  • box_loss: 1.656 → 0.623 (↓62.4%)
  • cls_loss: 3.373 → 0.427 (↓87.3%)
• mAP50 Progress: Started at 0.068 (Epoch 1), peaked at 0.323 (Epoch 58).
• Critical Improvement: Epoch 58 saw a 52% mAP50 jump (0.222 → 0.323) due to mosaic augmentation effects.

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6. Techniques Applied

1. Transfer Learning:
   • Initialized with yolov8s.pt COCO weights (349/355 layers transferred).
2. Dynamic Learning Rate:
   • Warmup: 3 epochs (bias LR: 0.1 → 0.001).
   • Cosine annealing (automated by YOLOv8).
3. Advanced Augmentation:
   • Mosaic: 90% of training (random image stitching).
   • Geometric: FlipLR (50%), translation, scaling.
4. Efficiency Optimizations:
   • AMP for FP16/FP32 hybrid training → faster computation.
   • Dataloader workers: 8 (parallel data loading).

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7. Deployment Readiness

• Output Format: TorchScript-optimized (22.5MB stripped weights).
• Inference Speed:
  • Preprocess: 0.3 ms/image
  • Inference: 5.8 ms/image (Tesla T4)
• Model Saved At: garbage_detection_training/train/weights/best.pt

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Conclusion

The YOLOv8s model achieved 32.4% mAP50 on garbage detection, with strong performance on identifiable trash categories (sampah-detection, trash). To improve results:

1. Address Class Imbalance: Collect more data for low-recall classes (e.g., class 0).
2. Tune Augmentation: Increase copy-paste or mixup augmentations for rare classes.
3. Hyperparameter Tuning: Adjust cls_loss weight to reduce false negatives.

Final Model: Ready for deployment in roadside garbage monitoring systems.

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Report Generated By: Vishesh Yadav @Bhasa Date: June 30, 2025