This repository is for our paper "Faster Task-oriented Online Fine-tuning", which contains the Efficient Training model zoo for fast training and fine-tuning.
Exploring how to adapt efficient neural networks to various downstream tasks online.
We provide our state-of-the-art Efficient Training model-zoo for fast training and fine-tuning to save computational cost.
Detailed performances of our ET-NAS:
| Model Name | MParam | GMac | Top-1 | Inference Time (ms) | Training Step Time (ms) | Checkpoints |
|---|---|---|---|---|---|---|
| ET-NAS-A | 2.6 | 0.23 | 62.06 | 5.30 | 14.74 | Google Drive |
| ET-NAS-B | 3.9 | 0.39 | 66.92 | 5.92 | 15.78 | Google Drive |
| ET-NAS-C | 7.1 | 0.58 | 71.29 | 8.94 | 26.28 | Google Drive |
| ET-NAS-D | 15.2 | 1.55 | 74.46 | 14.54 | 36.30 | Google Drive |
| ET-NAS-E | 21.4 | 2.61 | 76.87 | 25.34 | 61.95 | Google Drive |
| ET-NAS-F | 28.4 | 2.31 | 78.80 | 33.83 | 93.04 | Google Drive |
| ET-NAS-G | 49.3 | 5.68 | 80.41 | 53.08 | 133.97 | Google Drive |
| ET-NAS-H | 44.0 | 5.33 | 80.92 | 76.80 | 193.40 | Google Drive |
| ET-NAS-I | 72.4 | 13.13 | 81.38 | 94.60 | 265.13 | Google Drive |
| ET-NAS-J | 103.0 | 18.16 | 82.08 | 131.92 | 370.28 | Google Drive |
| ET-NAS-K | 87.3 | 27.51 | 82.42 | 185.75 | 505.00 | Google Drive |
| ET-NAS-L | 130.4 | 23.46 | 82.65 | 191.89 | 542.52 | Google Drive |
from etnas import ETNas, MODEL_MAPPINGS
model_name = "ET-NAS-A"
# construct a ET-NAS network
network = ETNas(MODEL_MAPPINGS[model_name])
# load pre-trained model weights
network.load_state_dict(torch.load("{}.pth".format(MODEL_MAPPINGS[model_name])))You can download alll pre-trained model directly from: Google Drive.
After unzipping the file, the file structure should be like:
ET-NAS
──ET-NAS-A
│ 2-_32_2-11-112-1121112.pth
├─ET-NAS-B
│ 031-_32_1-1-221-11121.pth
├─ET-NAS-C
│ 011-_32_2-211-2-111122.pth
├─ET-NAS-D
│ 031-_64_1-1-221-11121.pth
├─ET-NAS-E
│ 10001-_64_4-111-11122-1111111111111112.pth
├─ET-NAS-F
│ 011-_64_21-211-121-11111121.pth
├─ET-NAS-G
│ 10001-_64_4-111111111-211112111112-11111.pth
├─ET-NAS-H
│ 211-_64_41-211-121-11111121.pth
├─ET-NAS-I
│ 02031-a02_64_111-2111-21111111111111111111111-211.pth
├─ET-NAS-J
│ 211-_64_411-2111-21111111111111111111111-211.pth
├─ET-NAS-K
│ 02031-a02_64_1121-111111111111111111111111111-21111111211111-1.pth
└─ET-NAS-L
23311-a02c12_64_211-2111-21111111111111111111111-211.pth
where each checkpoint file is named with the corresponding architecture encoding and saved into a folder with the series name.
python test.py \
--model-name ET-NAS-A \
--data-dir imagenet \ # path to dataset, imagenet for example
--model-zoo-dir model_zoo \ # path to pre-trained models, modelz_zoo for example
--batch-size 256 \ # batch size on each card
--num-workers 4 \ # number of workers
--device cuda:0 # device to run the network, set "cuda" for gpus and "cpu" for cpu