Code Clause Internship Project 2
This project uses the YOLOv5 deep learning framework for object detection and classification to develop a system for identifying animal species in real-world images. The project involves collecting and labeling image datasets, training the YOLOv5 model, and integrating the model with a user interface for ease of use.
https://drive.google.com/drive/folders/1xBHb2l4Z70Z-oPXHR3QvZF3iaYZnSGx-?usp=sharing
The data preparation process for this project involved collecting image datasets of different animal species and labeling the images with bounding boxes around the animals. This labeling process is necessary for training the YOLOv5 model to accurately detect and classify animal species.
Various classes are collected
- Tiger
- Giraffe
- Bear
- Lion
- Elephant
- Deer
- Wolf
- Bull
- Monkey
- Leopord
- Rhinoceros
- Hippo
- Cattle The collected data was labelled
The data is split into train and validation which is of the ratio 80:20 The prepared dataset is uploaded into the drive
The yolov5 github repo was cloned into google colab The data is mounted onto the google drive The dataset is unzipped for further accessing All the requirements for yolov5 are installed
Mark the given config files as required the training of model is done with image size 415, batch size - 31, epochs -100 Weights of yolov5s is given
The inference files are uploaded (video, images) The inference with the weights of best.pt is done
The final animal species detection system accurately identifies animal species in real-world images with a high degree of accuracy. The system can detect multiple animal species in the same image and outputs the predicted animal species with corresponding bounding boxes.
This project demonstrates the potential of deep learning frameworks like YOLOv5 for object detection and classification in the field of animal species detection. The system developed in this project has practical applications in wildlife conservation, animal behavior studies, and more. The project can be extended to include more animal species and to improve the accuracy and efficiency of the detection system.
The detected image is displayed
Image
Video