🐄 Cow Detection Model v1

This repository contains code for a cow detection model using PyTorch and YOLO.

💻 Requirements

• GPU: The model requires a GPU to train efficiently. Please ensure that you have access to a GPU before proceeding.
• PyTorch Library: Refer to the PyTorch website to install the appropriate version based on your operating system. You can find the installation instructions here: https://pytorch.org/
• YOLO Library: You can install the YOLO library by running pip install ultralytics.
• yaml file: Give the access directory to the dataset.

📄 Dataset

The dataset used for training this model consists of images collected from three outdoor cattle farms in France using a UAV (Mavic 3 Enterprise or Mavic 3 Thermal). The flights were conducted at an altitude of 30, 60 or 100 meters in nadir position. Images and their corresponding labeling files are available on the Zenodo repository ICAERUS HE Project.

The following breeds with distinct body colors are present in the dataset:

• White: Charolaises in Jalogny farm
• Spotted: Prim'Holstein, Normandes in Mauron and Derval farms
• Black/red: Salers, along with some black Prim'Holstein in Mauron and Derval farms

Image size can be 4000x3000 or 5280x3956 depending on the drone used.

Data Preparation

All images with animals from the dataset were used (241 over 1148). Dataset was split into training, validation and test with a ratio of 70/20/10.

Model parameters

• pre-trained model: yolov8m.pt
• batch: 8
• optimizer: auto
• lr: 0.01
• momentum: 0.937

Results

Metrics:

• Precision: 0.67
• Recall: 0.60

An example of cow detection in a image from test population: