| license: apache-2.0 | |
| tags: | |
| - object-detection | |
| - yolov9 | |
| - litter-detection | |
| - waste-detection | |
| - littercam | |
| - computer-vision | |
| datasets: | |
| - taco | |
| - openlittermap | |
| base_model: yolov9-c | |
| # 🗑️ LitterCam — YOLOv9-C Waste Detection (6-hr run) | |
| Detects **10 classes of roadside litter** from CCTV/dashcam footage. | |
| ## 📦 Files | |
| | File | Description | | |
| |------|-------------| | |
| | `best.pt` | ⭐ **Best mAP checkpoint** | | |
| | `last.pt` | Latest epoch — resume training | | |
| | `best.onnx` | ONNX FP16 for edge deployment | | |
| ## 🚀 Quick Inference | |
| ```python | |
| from ultralytics import YOLO | |
| model = YOLO("best.pt") | |
| results = model("road.jpg", conf=0.35) | |
| results[0].show() | |
| ``` | |
| ## 🏷️ Classes | |
| `cigarette_butt` · `plastic_bottle` · `drinks_can` · `fast_food_packaging` | |
| `plastic_bag` · `coffee_cup` · `glass_bottle` · `paper_waste` | |
| `food_wrapper` · `general_litter` | |
| ## ⚙️ Training | |
| - **Arch:** YOLOv9-C | **Epochs:** 50 | **Img:** 640px | **Batch:** 16 | |
| - **GPU:** Kaggle Dual T4 | **Mode:** 6-hr max accuracy | |
| *Last updated: 2026-05-02 14:34 UTC* | |