Awesome

NASOA

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.

Faster Task-oriented Online Fine-tuning

Exploring how to adapt efficient neural networks to various downstream tasks online. overview

Efficient Training (ET-NAS) Model-Zoo

We provide our state-of-the-art Efficient Training model-zoo for fast training and fine-tuning to save computational cost.

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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

How to use ET-NAS

Use ET-NAS in your own project

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.

Example: Running test on Imagenet

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