---
tags:
- object-detection
- yolov4
- infrared-imagery
- wildlife-detection
- sage-grouse
- computer-vision
library_name: darknet
license: gpl-3.0
---

# 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](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](https://github.com/biasintelligence/INDECS)**.  
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

- **mAP@0.50**: **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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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