Model Card for Sage-Grouse Detection Model (YOLOv4)
This model detects sage-grouse in infrared drone imagery using YOLOv4.
Model Details
Model Description
This model is a YOLOv4 object detection model trained to identify sage-grouse in infrared drone videos and images. It is designed to aid in wildlife monitoring and conservation efforts by automating the detection process.
- Developed by: Ilya Buzytsky / Bias Intelligence Inc.
- Funded by: Bias Intelligence Inc.
- Shared by: Ilya Buzytsky / Bias Intelligence Inc.
- Model type: Object Detection (YOLOv4)
- Language(s) (NLP): N/A (Computer Vision)
- License: GPL-3.0
Model Sources
- Repository: https://github.com/biasintelligence/INDECS
- Paper: N/A (in peer review)
Uses
Intended Use
- Wildlife Monitoring: Automating sage-grouse detection for conservation efforts.
- Environmental Research: Studying habitat conditions using infrared imaging.
- Drone Surveys: Assisting biologists and researchers in identifying populations.
Out-of-Scope Use
- The model is not suitable for real-time processing in low-latency applications.
- The model is not designed for general-purpose object detection or use on non-infrared imagery.
- The model may not generalize well to infrared cameras or environments different from those in the training data.
Bias, Risks, and Limitations
The model’s performance may vary depending on the quality and characteristics of the infrared imagery. Factors such as image resolution, lighting conditions, and occlusions can affect detection accuracy. Additionally, because the model was trained on a specific dataset, it may not generalize well to significantly different geographies, environments, or sensor types.
Recommendations
Users should be aware of these limitations and carefully evaluate the model's performance on their specific dataset. It is recommended to:
- Use high-quality infrared imagery.
- Validate results with manual inspection.
- Fine-tune on additional data if necessary.
How to Get Started with the Model
Follow the installation and usage instructions provided in the INDECS repository.
Run the process_images.py
script to process infrared images for sage-grouse detection.
Training Details
Training Data
- Dataset Size: 450 infrared drone images.
- Source: Extracted from 12 drone flight videos captured across 11 distinct locations.
- Flight Patterns: Combination of linear and POI overflight patterns at 150-200 feet altitude.
- Resolution: Images were processed at 640×512 px (native video resolution).
- Annotation: Bounding boxes were manually labeled for each sage-grouse instance.
Training Procedure
Preprocessing
- Frame Extraction: Images were extracted from videos at their original resolution.
- Data Augmentation: None.
Evaluation
Metrics
The model was evaluated using Mean Average Precision (mAP) as the primary metric.
Results
- [email protected]: 98.42%
- Precision: 98%
- Recall: 98%
- F1-Score: 98%
- Average IoU: 81.85%
Summary
The model achieved an mAP of 98.42% on the test set, demonstrating high detection accuracy with strong localization (81.85% IoU) for sage-grouse in infrared aerial images.
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Used: NVIDIA RTX 3090 GPU, 16-core AMD Threadripper, 128 GB RAM.
- Training Duration: ~10 hours.
- Training Environment: Local workstation.
Technical Specifications
Model Architecture and Objective
- Architecture: YOLOv4 object detection network.
- Loss Function: CIoU loss.
- Non-Maximum Suppression (NMS): GreedyNMS (β = 0.6).
Compute Infrastructure
Hardware
- GPU: NVIDIA RTX 3090
- CPU: 16-core AMD Threadripper
- RAM: 128 GB
Software
- CUDA: 11.8
- cuDNN: 11.4
- Darknet Framework
- Python Version: 3.8