https://github.com/wandering007/nasnet-pytorch
A neat pytorch implementation of nasnet and the ported weights from tensorflow
https://github.com/wandering007/nasnet-pytorch
nasnet pytorch
Last synced: 7 months ago
JSON representation
A neat pytorch implementation of nasnet and the ported weights from tensorflow
- Host: GitHub
- URL: https://github.com/wandering007/nasnet-pytorch
- Owner: wandering007
- License: mit
- Created: 2018-03-20T12:39:35.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-12-05T08:57:54.000Z (almost 5 years ago)
- Last Synced: 2024-08-01T22:50:07.544Z (over 1 year ago)
- Topics: nasnet, pytorch
- Language: Python
- Size: 8.79 KB
- Stars: 74
- Watchers: 4
- Forks: 17
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-pytorch : https://github.com/wandering007/nasnet-pytorch
- awesome-image-classification - unofficial-pytorch : https://github.com/wandering007/nasnet-pytorch
- awesome-AutoML-and-Lightweight-Models - wandering007/nasnet-pytorch
README
## A neat pytorch implementation of NASNet
The performance of the ported models on ImageNet (Accuracy):
| Model Checkpoint | Million Parameters | Val Top-1 | Val Top-5 |
| ------------------- | ------------------ | --------- | --------- |
| NASNet-A_Mobile_224 | 5.3 | 70.2 | 89.4 |
| NASNet-A_large_331 | 88.9 | 82.3 | 96.0 |
The slight performance drop may be caused by the different spatial padding methods between tensorflow and pytorch.
The porting process is done by `tensorflow_dump.py` and `pytorch_load.py`, modified from [Cadene's project](https://github.com/Cadene/tensorflow-model-zoo.torch/tree/master/nasnet). Note that NASNets with the original performance can be found there.
You can evaluate the models by running `imagenet_eval.py`, e.g. evaluate the NASNet-A_Mobile_224 ported model by
```shell
python imagenet_eval.py --nas-type mobile --resume /path/to/modelfile --gpus 0 --data /path/to/imagenet_root_dir
```
The ported model files are provided: [NASNet-A_Mobile_224, NASNet-A_large_331](https://www.dropbox.com/sh/ng93kp7f7ypat73/AABUQhImioJ2saQ3N-qWzrJga?dl=0).
Future work:
- add drop path for training
- more nasnet model settings