Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Differentiable architecture search for convolutional and recurrent networks
| Date | Stars |
|---|---|
| 2026-07-24 | 3996 |
| 2026-07-25 | 3996 |
| 2026-07-28 | 3996 |
| 2026-07-30 | 3996 |
| 2026-08-06 | 3996 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Differentiable Architecture Search
Code accompanying the paper
> [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055)\
> Hanxiao Liu, Karen Simonyan, Yiming Yang.\
> _arXiv:1806.09055_.
<p align="center">
<img src="img/darts.png" alt="darts" width="48%">
</p>
The algorithm is based on continuous relaxation and gradient descent in the architecture space. It is able to efficiently design high-performance convolutional architectures for image classification (on CIFAR-10 and ImageNet) and recurrent architectures for language modeling (on Penn Treebank and WikiText-2). Only a single GPU is required.
## Requirements
```
Python >= 3.5.5, PyTorch == 0.3.1, torchvision == 0.2.0
```
NOTE: PyTorch 0.4 is not supported at this moment and would lead to OOM.
## Datasets
Instructions for acquiring PTB and WT2 can be found [here](https://github.com/salesforce/awd-lstm-lm). While CIFAR-10 can be automatically downloaded by torchvision, ImageNet needs to be manually downloaded (preferably to a SSD) following the instructions [here](https://github.com/pytorch/examples/tree/master/imagenet).
## Pretrained models
The easist way to get started is to evaluate our pretrained DARTS models.
**CIFAR-10** ([cifar10_model.pt](https://drive.google.com/file/d/1Y13i4zKGKgjtWBdC0HWLavjO7wvEiGOc/view?usp=sharing))
```
cd cnn && python test.py --auxiliary --model_path cifar10_model.pt
```
* Expected result: 2.63% test error rate with 3.3M model params.
**PTB** ([ptb_model.pt](https://drive.google.com/file/d/1Mt_o6fZOlG-VDF3Q5ModgnAJ9W6f_av2/view?usp=sharing))
```
cd rnn && python test.py --model_path ptb_model.pt
```
* Expected result: 55.68 test perplexity with 23M model params.
**ImageNet** ([imagenet_model.pt](https://drive.google.com/file/d/1AKr6Y_PoYj7j0Upggyzc26W0RVdg4CVX/view?usp=sharing))
```
cd cnn && python test_imagenet.py --auxiliary --model_path imagenet_model.pt
```
* Expected result: 26.7% top-1 error and 8.7% top-5 error with 4.7M model params.
## Architecture search (using small proxy models)
To carry out architecture search using 2nd-order approximation, run
```
cd cnn && python train_search.py --unrolled # for conv cells on CIFAR-10
cd rnn && python train_search.py --unrolled # for recurrent cells on PTB
```
Note the _validation performance in this step does not indicate the final performance of the architecture_. One must train the obtained genotype/architecture from scratch using full-sized models, as described in the next section.
Also be aware that different runs would end up with different local minimum. To get the best result, it is crucial to repeat the search process with different seeds and select the best cell(s) based on validation performance (obtained by training the derived cell from scratch for a small number of epochs). Please refer to fig. 3 and sect. 3.2 in our arXiv paper.
<p align="center">
<img src="img/progress_convolutional_normal.gif" alt="progress_convolutional_normal" width="29%">
<img src="img/progress_convolutional_reduce.gif" alt="progress_convolutional_reduce" width="35%">
<img src="img/progress_recurrent.gif" alt="progress_recurrent" width="33%">
</p>
<p align="center">
Figure: Snapshots of the most likely normal conv, reduction conv, and recurrent cells over time.
</p>
## Architecture evaluation (using full-sized models)
To evaluate our best cells by training from scratch, run
```
cd cnn && python train.py --auxiliary --cutout # CIFAR-10
cd rnn && python train.py # PTB
cd rnn && python train.py --data ../data/wikitext-2 \ # WT2
--dropouth 0.15 --emsize 700 --nhidlast 700 --nhid 700 --wdecay 5e-7
cd cnn && python train_imagenet.py --auxiliary # ImageNet
```
Customized architectures are supported through the `--arch` flag once specified in `genotypes.py`.
The CIFAR-10 result at the end of training is subject to variance due to the non-determinism of cuDNN back-prop kernels. _It would be misleadinExcerpt of 5,064 characters
Read on GitHub
67
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d497ed9a87025fc0, topic:deep-learning, topic:pytorch
matched fp:d497ed9a87025fc0, topic:image-classification, readme:image classification