README.md

---
license: mit
task_categories:
- graph-ml
- image-classification
pretty_name: DeepNets
size_categories:
- 1M<n<10M
tags:
- graph
- computational-graph
- hypernetwork
---

This is a copy of the **DeepNets-1M** dataset originally released at https://github.com/facebookresearch/ppuda under the MIT license.

The dataset presents diverse computational graphs (1M training and 1402 evaluation) of neural network architectures used in image classification.
See detailed description in the [Parameter Prediction for Unseen Deep Architectures](https://arxiv.org/abs/2110.13100) paper.


There are four files in this dataset:
- deepnets1m_eval.hdf5;    # 16 MB (md5: 1f5641329271583ad068f43e1521517e)
- deepnets1m_meta.tar.gz;  # 35 MB (md5: a42b6f513da6bbe493fc16a30d6d4e3e), run `tar -xf deepnets1m_meta.tar.gz` to unpack it before running any code reading the dataset
- deepnets1m_search.hdf5;  # 1.3 GB (md5: 0a93f4b4e3b729ea71eb383f78ea9b53)
- deepnets1m_train.hdf5;   # 10.3 GB (md5: 90bbe84bb1da0d76cdc06d5ff84fa23d)

## GHN-2

- paper: [Parameter Prediction for Unseen Deep Architectures](https://arxiv.org/abs/2110.13100)
- code: https://github.com/facebookresearch/ppuda
- models: https://github.com/facebookresearch/ppuda/tree/main/checkpoints 
  
## GHN-3

- paper: [Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?](https://arxiv.org/abs/2303.04143) training an improved GHN on this dataset
- code: https://github.com/SamsungSAILMontreal/ghn3
- models: https://huggingface.co/SamsungSAILMontreal/ghn3


## Citation

If you use this dataset, please cite it as:

```
@inproceedings{knyazev2021parameter,
  title={Parameter Prediction for Unseen Deep Architectures},
  author={Knyazev, Boris and Drozdzal, Michal and Taylor, Graham W and Romero-Soriano, Adriana},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}  
}
```