Metadata-Version: 2.1
Name: model-index
Version: 0.1.3
Summary: Create a source of truth for ML model results and browse it on Papers with Code
Home-page: https://github.com/paperswithcode/model-index
Author: Robert Stojnic
Author-email: hello@paperswithcode.com
Maintainer: Robert Stojnic
Maintainer-email: hello@paperswithcode.com
License: MIT
Description: # model-index: maintain a source of truth for ML models
        
        `model-index` has two goals:
        - Make it easy to maintain a source-of-truth index of Machine Learning model metadata 
        - Enable the community browse this model metadata on [Papers with Code](https://paperswithcode.com/)
        
        The main design principle of `model-index` is **flexibility**. You can store your model metadata however is the
        most convenient for you - as JSONs, YAMLs or as annotations inside markdown. `model-index` provides a convenient
        way to collect all this metadata into a single file that's browsable, searchable and comparable.
        
        You can use this library locally or choose to upload the metadata to [Papers with Code](https://paperswithcode.com)
        to have your library featured on the website. 
        
        ## How it works
        
        There is a root file for the model index: `model-index.yml` that links to (or contains) metadata. 
        
        ```yaml
        Models:
          - Name: Inception v3
            Metadata:
              FLOPs: 11462568384
              Parameters: 23834568
              Epochs: 90
              Batch Size: 32
              Training Data: ImageNet  
              Training Techniques: 
                - RMSProp
                - Weight Decay
                - Gradient Clipping
                - Label Smoothing
              Training Resources: 8x V100 GPUs
              Architecture:
                - Auxiliary Classifier
                - Inception-v3 Module
            Results:
              - Task: Image Classification
                Dataset: ImageNet
                Metrics:
                  Top 1 Accuracy: 74.67%
                  Top 5 Accuracy: 92.1%
            Paper: https://arxiv.org/abs/1512.00567v3
            Code: https://github.com/rwightman/pytorch-image-models/blob/timm/models/inception_v3.py#L442
            Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth 
            README: docs/inception-v3-readme.md
        ```
        
        You can add any fields you like, but the ones above have a standard meaning across different models and libraries. 
        
        ### Storing metadata in markdown files
        
        Metadata can also be directly stored in model's README file. For example in this `docs/rexnet.md` file:
        
        ```markdown
        <!--
        Type: model-index
        Name: RexNet
        Metadata: 
          Epochs: 400
          Batch Size: 512
        Paper: https://arxiv.org/abs/2007.00992v1
        -->
        
        # Summary
        
        Rank Expansion Networks (ReXNets) follow a set of new design 
        principles for designing bottlenecks in image classification models.
        
        ## Usage
        
        import timm
        m = timm.create_model('rexnet_100', pretrained=True)
        m.eval()
        ```
        
        In this case, you just need to include this markdown file into the global `model-index.yml` file:
        
        ```yaml
        Models:
          - docs/rexnet.md
        ```
        
        ## Get started
        
        Check out our [official documentation](https://model-index.readthedocs.io/en/latest/) on how to get started. 
        
        ## Uploading to Papers with Code
        
        To feature your library on Papers with Code, get in touch with `hello@paperswithcode.com` and the model index
        of your library will be automatically included into Papers with Code. 
        
        
        
        
        
        
        
        
Platform: Windows
Platform: POSIX
Platform: MacOSX
Description-Content-Type: text/markdown
