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Computer Vision
YOLOv8 Animals Detection
80-class animal detection with YOLOv8n. mAP@0.5=0.668, mAP@0.5:0.95=0.560. Best: Tiger (0.967), Sparrow (0.953). Challenging: Squid (0.009). ONNX (12.3 MB). 29,071 images across 80 species.
0.6681
mAP@0.5 (overall)
0.967
Best class (Tiger)
0.5596
mAP@0.5:0.95
3.0ms / image
Inference speed
Dataset
22,566 images, 80 species, CVAT annotated
Approach
CVAT→YOLO conversion → YOLOv8n fine-tuning → ONNX export for edge deployment
Tech Stack
PythonYOLOv8 (Ultralytics)ONNXCVATAdamW
Keywords
YOLOv8Object Detection80-classWildlifeONNXEdge Deployment
Visualizations5 Charts
Deep Dive
80-class animal detection pipeline using YOLOv8n.
Dataset
- ▸22,566 annotated images: 18,053 train / 4,513 val + 6,505 test
- ▸80 animal species: Bear → Zebra (alphabetically)
- ▸CVAT XML → YOLO normalized format (multi-threaded, 4 CPU workers)
- ▸Class imbalance: 2 images (rare species) to 100+ (common species)
Training YOLOv8n (3.15M params, 73 layers) — 50 epochs, batch=16, imgsz=640, AdamW (lr=1e-3), patience=10
Overall Performance
| Metric | Value |
|---|---|
| mAP@0.5 | 0.6681 |
| mAP@0.5:0.95 | 0.5596 |
| Precision | 0.6706 |
| Recall | 0.6478 |
| Inference speed | 3.0ms / image |
Per-Class Highlights
| Best Species | mAP@0.5 | Worst Species | mAP@0.5 |
|---|---|---|---|
| Tiger | 0.967 | Squid | 0.009 |
| Sparrow | 0.953 | Seahorse | 0.000 |
| Rhinoceros | 0.940 | Hamster | 0.230 |
| Hedgehog | 0.942 |
Performance variance is almost entirely driven by training sample count. The 3.0ms inference speed makes this viable for real-time wildlife monitoring applications.
Deployment: ONNX export (12.3 MB) for edge devices.