This is the offical pytorch implementation for paper "Beyond Visual Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks".

Human-centric visual tasks have attracted increasing research attention due to their widespread applications. SOLIDER is a framework used to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent. The full name of SOLIDER is a semantic controllable self-supervised learning framework. Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation. Meanwhile, SOLIDER introduces a conditional network with a semantic controller. After the model is trained, users can send values to the controller to produce representations with different ratios of semantic information, which can fit different needs of downstream tasks.

This codebase has been developed with python version 3.7, PyTorch version 1.7.1, CUDA 10.1 and torchvision 0.8.2.

Download LUPerson from its offical link and unzip it.

• Choice 1. To train SOLIDER from scratch, please run:

sh run_solider.sh

• Choice 2. Training SOLIDER from scratch may take a long time. To speed up the training, you can train a DINO model first, and then finetune it with SOLIDER, as follows:

sh run_dino.sh
sh resume_solider.sh

There is a demo to run the trained SOLIDER model, which can be embedded into the inference or the downstream task finetuning.

python demo.py

• All the models are trained on the whole LUPerson dataset.

• The training codes of downstream tasks will be released soon.

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

• damo-cv/TransReID-SSL
• facebookresearch/dino.