Clothes Segmentation

This project provides a solution for segmenting clothes into 18 categories using DINO, ViT and UNet models.

Gradio is used for building a web interface and Weights & Biases for experiments tracking.

Installation

Clone the repository:

git clone https://github.com/your-project/clothes-segmentation.git
cd plant-classifier

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate

To train a model, specify one of the following using the --model argument: dino, vit or unet.

python src/train.py --model dino
python src/train.py --model vit
python src/train.py --model unet

You can also adjust other parameters, such as the number of epochs, batch size, and learning rate, by adding additional arguments. For example:

python src/train.py --model unet --num-epochs 20 --batch-size 16 --learning-rate 0.001

python app.py

Once the interface is running, you can select a model, upload an image and view the segmentation mask.

Model	Test Micro Recall	Test Micro Precision	Test Macro Precision	Test Macro Recall	Test Accuracy	Test Loss	Train Micro Recall	Train Micro Precision	Train Macro Precision	Train Macro Recall	Train Accuracy	Train Loss
DINO	0.94986	0.94986	0.71364	0.67052	0.94986	0.53124	0.97019	0.97019	0.78185	0.72336	0.97019	0.30441
ViT	0.9358	0.9358	0.63939	0.58365	0.9358	0.71193	0.96734	0.96734	0.74418	0.66295	0.96734	0.31166
UNet	0.95798	0.95798	0.76354	0.7289	0.95798	0.56764	0.98035	0.98035	0.82934	0.82688	0.98035	0.25301