Deep Learning & 6th Semester Coursework (IILM26)

This repository contains coursework and projects for the 6th semester at IILM, specializing in Deep Learning applications.

Project Structure

The repository is organized into the following main directories:

1. yolov8-trial

This directory contains experiments and implementations related to the YOLOv8 object detection model.

• Purpose: Training and testing YOLOv8 on specific datasets.
• Dataset: The model is trained on an accident detection dataset.
  • Download: You can download the required dataset using the kaggle_dataset.py script provided in this directory.
• Key Files:
  • kaggle_dataset.py: Script to fetch the training data.
  • Model training and inference scripts (e.g., detection.py, accuracy.py).

2. VITH-SEM

This directory houses classworks and assignments for the 6th semester.

• Content: Various lab exercises, assignments, and minor projects related to the semester curriculum.

Setup and Usage

To use the YOLOv8 trial scripts:

1. Navigate to the yolov8-trial directory.
2. Install necessary dependencies (ensure you have a Python environment set up).
3. Run python kaggle_dataset.py to download the dataset.
4. Use the training or detection scripts as needed.

Notes

• Ensure you have the content of the dataset folder before running training scripts. The folder is excluded from version control to save space.