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---
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}
}
``` |