FORK

I forked the cloth segmentation repo, trained the model for 4 epochs(https://drive.google.com/file/d/1aS4gB2UucK1twuU5ROg_MYRIxm3jmk1v/view?usp=drive_link) and made some adjustments for easy setup (inference).

Usage

• install dependencies: pip install -r requirements.txt
• download model checkpoint: gdown --output checkpoint_u2net.pth 1aS4gB2UucK1twuU5ROg_MYRIxm3jmk1v
• put images into input_images
• inference to output_images on cpu or gpu:
  • DEVICE=cpu python cloth_segmentation/infer.py
  • python infer.py

Original Readme:

Clothes Segmentation using U2NET

This repo contains training code, inference code and pre-trained model for Cloths Parsing from human portrait.
Here clothes are parsed into 3 category: Upper body(red), Lower body(green) and Full body(yellow)

This model works well with any background and almost all poses. For more samples visit samples.md

Techinal details

• U2NET : This project uses an amazing U2NET as a deep learning model. Instead of having 1 channel output from u2net for typical salient object detection task it outputs 4 channels each respresting upper body cloth, lower body cloth, fully body cloth and background. Only categorical cross-entropy loss is used for a given version of the checkpoint.

• Dataset : U2net is trained on 45k images iMaterialist (Fashion) 2019 at FGVC6 dataset. To reduce complexity, I have clubbed the original 42 categories from dataset labels into 3 categories (upper body, lower body and full body). All images are resized into square ¯\_(ツ)_/¯ 768 x 768 px for training. (This experiment was conducted with 768 px but around 384 px will work fine too if one is retraining on another dataset).

Training

• For training this project requires,

•   PyTorch > 1.3.0
•   tensorboardX
•   gdown

• Download dataset from this link, extract all items.
• Set path of train folder which contains training images and train.csv which is label csv file in options/base_options.py
• To port original u2net of all layer except last layer please run python setup_model_weights.py and it will generate weights after model surgey in prev_checkpoints folder.
• You can explore various options in options/base_options.py like checkpoint saving folder, logs folder etc.
• For single gpu set distributed = False in options/base_options.py, for multi gpu set it to True.
• For single gpu run python train.py
• For multi gpu run
   python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=4 --use_env train.py
  Here command is for single node, 4 gpu. Tested only for single node.
• You can watch loss graphs and samples in tensorboard by running tensorboard command in log folder.

Testing/Inference

• Download pretrained model from this link(165 MB) in trained_checkpoint folder.
• Put input images in input_images folder
• Run python infer.py for inference.
• Output will be saved in output_images

OR

• Inference in colab from here

Acknowledgements

• U2net model is from original u2net repo. Thanks to Xuebin Qin for amazing repo.
• Complete repo follows structure of Pix2pixHD repo