How to Utilize Self Correction for Human Parsing

Are you ready to dive into the world of human parsing? Our Self Correction for Human Parsing solution is an out-of-box extractor that has topped every human parsing challenge, including single, multiple, and video tasks! In this guide, we’ll walk you step-by-step on how to utilize our tools to enhance your own projects.

Features of Our Solution

• Out-of-box human parsing extractor for various downstream applications.
• Pretrained models on three popular single-person human parsing datasets.
• Training and inference code included.
• Simple but effective extensions available for multi-person and video human parsing tasks.

System Requirements

To get started, ensure you have the necessary setup ready. Follow these commands:

conda env create -f environment.yaml
conda activate schp
pip install -r requirements.txt

Using the Simple Out-of-Box Extractor

To kick off your journey, you can use our trained SCHP models on your own images to extract human parsing representations. Here’s a helpful analogy: think of the model like a master chef who has mastered different cuisines. Based on your taste (or task), you will pick the chef who specializes in that area and has all the secret recipes (or datasets) to create the perfect dish (or output).

Pretrained Models and Datasets

We’ve provided state-of-the-art models for three popular datasets:

• LIP – The largest dataset with over 50,000 images. Example model: exp-schp-201908261155-lip.pth. mIoU: 59.36%.
• ATR – Focused on fashion AI with over 17,000 images. Example model: exp-schp-201908301523-atr.pth. mIoU: 82.29%.
• Pascal-Person-Part – A smaller dataset with a focus on body part segmentation. Example model: exp-schp-201908270938-pascal-person-part.pth. mIoU: 71.46%.

Extracting Human Parsing Representations

Follow these steps to extract human representations:

1. Put your image in the INPUT_PATH folder.
2. Download a pretrained model.
3. Run the command below:

python simple_extractor.py --dataset [DATASET] --model-restore [CHECKPOINT_PATH] --input-dir [INPUT_PATH] --output-dir [OUTPUT_PATH]

Here, [DATASET] could be lip, atr, or pascal based on your selection. The output images will get saved with the same file names in your specified OUTPUT_PATH.

Dataset Preparation

For optimal performance, download the LIP dataset and structure it as follows:

data
└── LIP
    ├── train_images
    ├── val_images
    ├── train_segmentations
    ├── val_segmentations
    ├── train_id.txt
    └── val_id.txt

Training and Evaluation

To train the model, simply execute:

python train.py

The trained model will save in the .log directory. For evaluation, use the command:

python evaluate.py --model-restore [CHECKPOINT_PATH]

Troubleshooting Tips

If you encounter issues during installation or usage, consider the following:

• Ensure your environment is correctly set up with all dependencies installed.
• Double-check the file paths for your images and model checkpoints.
• If the output isn’t as expected, evaluate the data fed into the model. Experiment with different datasets and images.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.