YOLO-Clothing-Detection

In this repo YOLO model is finetuned to detect clothing using a reduced version of fashionpedia. It contains four categories:

• Clothing
• Shoes
• Bags
• Accessories

The class distribution is shown in the figure below. As it can be seen clothing is the class with the highest number of instances and the amount of data between the classes is high.

How to run the codes

First you need to run data_preprocessing.py to change the format of data to desired format of ultralytics library.

The output dataset directory structure will be like the following:

datasets/
└── fashion_dataset/
    ├── images/
    │   ├── train/
    │   ├── val/
    │   └── test/
    └── labels/
        ├── train/
        ├── val/
        └── test/

Then you can train the model using model_training.py. Lastly to test models on images you can use model_test notebook. Here are some results from the trained model on test set:

Helpful resources

YOLO explanation: https://www.youtube.com/watch?v=9s_FpMpdYW8&list=WL&index=20
YOLO explanation in depth: https://www.youtube.com/watch?v=svn9-xV7wjk&list=WL&index=19
Mean Average Precision (mAP) explanation and coding: https://www.youtube.com/watch?v=FppOzcDvaDI
Intersection Over Union (IoU) explanation and coding: https://www.youtube.com/watch?v=XXYG5ZWtjj0 (High noise warning for the start of the video :D )