Clothing detection using YOLOv3, RetinaNet, Faster RCNN in ModaNet and DeepFashion2 datasets.

Datasets

• DeepFashion2 dataset: https://github.com/switchablenorms/DeepFashion2

• ModaNet dataset: https://github.com/eBay/modanet

Models

• Faster RCNN, RetinaNet and Mask RCNN (only detection) trained with maskrcnn-benchmark https://github.com/facebookresearch/maskrcnn-benchmark/. To use this models please follow INSTALL instruccions in that repo and do the setup in the root folder of this repo. Not neccessary to use pytorch-nightly, you can use pytorch 1.2 instead.

• YOLOv3 trained with Darknet framework: https://github.com/AlexeyAB/darknet

• TridenNet trained with simpledet framework https://github.com/TuSimple/simpledet

• To do inference use a pytorch implementation of YOLOv3: https://github.com/eriklindernoren/PyTorch-YOLOv3.

• All the models trained with Resnet50 backbone, except YOLOv3 with Darknet53

Weights

All weights and config files are in https://drive.google.com/drive/folders/1jXZZc5pp2OJCtmQYelzDgPzyuraAdxXP?usp=sharing

Using

• Use new_image_demo.py , and choose dataset, and model.
• Use YOLOv3Predictor class for YOLOv3 and Predictor class for Faster and RetinaNet and Mask.

Coming soon

• Update use of retrieval.