| --- |
| 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} |
| } |
| ``` |
