Create a source of truth for ML model results and browse it on Papers with Code
Project 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
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 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.
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:
<!--
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:
Models:
- docs/rexnet.md
Get started
Check out our official documentation 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.
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