Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- object-detection
+- detectron2
+- wildlife
+- animals
+- faster-rcnn
+- inception
+datasets:
+- custom-wildlife-dataset
+metrics:
+- AP
+- AP50
+- AP75
+model-index:
+- name: inception-wildlife-detector-detectron2
+  results:
+  - task:
+      type: object-detection
+      name: Object Detection
+    dataset:
+      type: custom-wildlife-dataset
+      name: Wildlife Detection Dataset
+    metrics:
+    - type: AP
+      value: 45.7
+      name: Average Precision
+    - type: AP50
+      value: 81.8
+      name: AP at IoU=0.50
+    - type: AP75
+      value: 47.7
+      name: AP at IoU=0.75
+---
+
+# Wildlife Detector - Detectron2
+
+A Faster R-CNN model with optimized Inception v1 backbone for detecting 10 wildlife species.
+
+## Classes
+- Antelope
+- Buffalo  
+- Elephant
+- Giraffe
+- Gorilla
+- Leopard
+- Lion
+- Rhino
+- Wolf
+- Zebra
+
+## Performance
+
+| Metric | Value |
+|--------|--------|
+| AP     | 45.7%  |
+| AP50   | 81.8%  |
+| AP75   | 47.7%  |
+
+### Per-Class Performance (AP)
+| Animal   | AP    | Animal   | AP    |
+|----------|-------|----------|-------|
+| Buffalo  | 58.9% | Elephant | 58.5% |
+| Gorilla  | 51.2% | Leopard  | 49.4% |
+| Wolf     | 48.0% | Antelope | 46.4% |
+| Rhino    | 44.1% | Zebra    | 43.7% |
+| Lion     | 31.9% | Giraffe  | 24.5% |
+
+## Usage
+
+```python
+import torch
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from detectron2 import model_zoo
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="mynane/inception-wildlife-detector-detectron2", 
+    filename="pytorch_model.bin"
+)
+
+# Setup config
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_C4_3x.yaml"))
+cfg.MODEL.WEIGHTS = model_path
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 10
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+
+# Create predictor
+predictor = DefaultPredictor(cfg)
+
+# Inference
+import cv2
+image = cv2.imread("wildlife_image.jpg")
+outputs = predictor(image)
+```
+
+## Model Details
+- **Architecture**: Faster R-CNN with Optimized Inception v1 backbone
+- **Framework**: Detectron2
+- **Input Size**: 800x1333 (min x max)
+- **Confidence Threshold**: 0.5