metadata

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

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