Metadata-Version: 2.1
Name: people-segmentation
Version: 0.0.2
Summary: High quality model for people segmentation.
Home-page: https://github.com/ternaus/people_segmentation
Author: Vladimir Iglovikov
License: MIT
Description: # Binary segmentation of people
        
        ![](https://habrastorage.org/webt/bc/eg/g8/bcegg8zdgd-co-lip6hxn976jdm.jpeg)
        
        ## Data
        
        ### Train set:
        
        * Mapillary Vistas Commercial 1.2 (train)
        * COCO (train)
        * Pascal VOC (train)
        * [Human Matting](https://www.kaggle.com/laurentmih/aisegmentcom-matting-human-datasets/)
        
        ### Validation set:
        * Mapillary Vistas Commercial 1.2 (val)
        * COCO (val)
        * Pascal VOC (val)
        * Supervisely
        
        To convert datasets to the format:
        
        ```
        training
            coco
            matting_humans
            pascal_voc
            vistas
        
        validation
            coco
            pascal_voc
            supervisely
            vistas
        ```
        use this set of [scipts](https://github.com/ternaus/iglovikov_helper_functions/tree/master/iglovikov_helper_functions/data_processing/prepare_people_segmentation).
        
        ## Training
        
        ### Define the config.
        Example at [people_segmentation/configs](people_segmentation/configs)
        
        You can enable / disable datasets that are used for training and validation.
        
        ### Define the environmental variable `TRAIN_PATH` that points to the folder with train dataset.
        Example:
        ```bash
        export TRAIN_PATH=<path to the tranining folder>
        ```
        
        ### Define the environmental variable `VAL_PATH` that points to the folder with validation dataset.
        Example:
        ```bash
        export VAL_PATH=<path to the validation folder>
        ```
        
        ### Run training
        ```
        python -m people_segmentation.train -c <path to config>
        ```
        
        You can check the loss and validation curves for the configs from [people_segmentation/configs](people_segmentation/configs)
        at [W&B dashboard](https://wandb.ai/ternaus/people_segmentation-people_segmentation)
        
        ### Run Inference
        
        ```bash
        python -m torch.distributed.launch --nproc_per_node=<num_gpu> people_segmentation/inference.py \
                                           -i <path to images> \
                                           -c <path to config> \
                                           -w <path to weights> \
                                           -o <output-path> \
                                           --fp16
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: test
