metadata
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
- 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
- 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:
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)