wild_animal_detection_yolos
Overview
This model is a fine-tuned version of YOLOS (You Only Look at One Sequence) specifically trained for the detection of endangered wildlife in forest environments. It is designed to process camera trap footage and drone imagery to assist conservationists.
Model Architecture
The model leverages a pure Vision Transformer (ViT) architecture adapted for object detection. Unlike traditional CNN-based detectors, YOLOS treats detection as a sequence-to-sequence problem, making it highly efficient for capturing global context in complex forest backgrounds.
Intended Use
- Wildlife Monitoring: Automating the census of animal populations in national parks.
- Poaching Prevention: Real-time alerts when large mammals are detected in restricted zones.
- Biodiversity Research: Sorting through thousands of hours of static camera trap images.
Limitations
- Camouflage: Performance may decrease if the animal is heavily obscured by dense foliage.
- Low Light: Accuracy is lower on grainy infrared night-time footage compared to daylight.
- Small Objects: Difficulties detecting very young or small animals at long distances.
