---
license: mit
tags:
  - object-detection
  - detectron2
  - wildlife
  - computer-vision
  - pytorch
  - bounding-box
model-index:
  - name: Wildlife Detector (Detectron2)
    results:
      - task:
          name: Object Detection
          type: object-detection
        dataset:
          name: Wildlife Custom Dataset
          type: custom
        metrics:
          - name: AP (IoU=0.50:0.95)
            type: mean_average_precision
            value: 70.0
          - name: AP50
            type: mean_average_precision
            value: 97.8
          - name: AP75
            type: mean_average_precision
            value: 84.3
          - name: APs (Small Objects)
            type: mean_average_precision
            value: 10.2
          - name: APm (Medium Objects)
            type: mean_average_precision
            value: 54.0
          - name: APl (Large Objects)
            type: mean_average_precision
            value: 70.8
---

# 🐾 Wildlife Detector (Detectron2)

A fine-tuned Faster R-CNN object detection model trained with Detectron2 to identify 8 species of wild animals from real-world images. This model is suitable for conservation applications, automated image tagging, and wildlife monitoring.

## 📦 Model Details

- **Model Type**: Faster R-CNN (ResNet-101 + FPN)
- **Framework**: Detectron2 (Facebook AI)
- **Task**: Object Detection (bounding box)
- **Classes**: Antelope, Lion, Elephant, Zebra, Gorilla, Wolf, Leopard, Giraffe

## 📈 Evaluation

| Metric        | Score    |
|---------------|----------|
| mAP (0.5:0.95) | 70.0     |
| AP50          | 97.8     |
| AP75          | 84.3     |
| AP (small)    | 10.2     |
| AP (medium)   | 54.0     |
| AP (large)    | 70.8     |

### Per-Class Performance

| Class      | AP (%) |
|------------|--------|
| Antelope   | 67.4   |
| Lion       | 68.8   |
| Elephant   | 78.3   |
| Zebra      | 66.7   |
| Gorilla    | 70.8   |
| Wolf       | 72.1   |
| Leopard    | 72.1   |
| Giraffe    | 64.0   |

## 🧪 How to Use

To run inference:

```python
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2 import model_zoo

cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"))
cfg.MODEL.WEIGHTS = "path/to/model_final.pth"
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8

predictor = DefaultPredictor(cfg)
outputs = predictor(your_image)