Yolov5-Model

Exploring YOLO Model

Objective:

To annotate images of fruits in a dataset and generate a YOLO model for object detection.

Key Steps:

1. Annotate Images:

   • Access the dataset containing images of fruits.
   • Annotate the images using a tool like LabelImg or Labelbox.
   • Assign class names to each annotated image.

2. Prepare Dataset:

   • Export the annotated dataset to a format compatible with PyTorch.
   • Copy the generated PyTorch code for the dataset.

3. Set Up Notebook:

   • Clone the YOLO version 5 repository.
   • Import necessary libraries into the notebook.

4. Load Dataset:

   • Paste the PyTorch code into the notebook.
   • Specify the directory path for the dataset.
   • Separate the dataset into test and training images.

5. Train YOLO Model:

   • Run the YOLO model training script for a specified number of epochs.
   • Utilize multiple workers (e.g., 8 workers) to reduce training time.

6. Test Model:

   • Evaluate the trained YOLO model on test images.
   • Verify the model's detection performance on the test images.

Cautionary Notes:

• Ensure proper annotation of images to avoid misclassification during model training.
• Double-check the dataset path and class details to prevent errors in model training.
• Monitor training progress and adjust parameters if necessary for optimal results.

Tips for Efficiency:

• Use efficient annotation tools to speed up the annotation process.
• Organize the dataset neatly with clear class labels for easy model training.
• Utilize multiple workers during training to accelerate the process and save time.

By following these steps, you can effectively annotate a fruits dataset and train a YOLO model for accurate object detection.

Link to Loom

https://www.loom.com/share/f3bfdd65bae34a5f917cba12cb3ee845