PyTorch (Generic) Efficient Networks
A 'generic' implementation of EfficientNet, MobileNet, etc. that covers most of the compute/parameter efficient architectures derived from the MobileNet V1/V2 block sequence, including those found via automated neural architecture search. All models are implemented by the same class, with string based architecture definitions to configure the block layouts (idea from here)
Models
Implemented models include:
- EfficientNet (B0-B7) (https://arxiv.org/abs/1905.11946) -- validated, compat with TF weights
- EfficientNet-EdgeTPU (S, M, L) (https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html) --validated w/ TF weights
- MixNet (https://arxiv.org/abs/1907.09595) -- validated, compat with TF weights
- MNASNet B1, A1 (Squeeze-Excite), and Small (https://arxiv.org/abs/1807.11626)
- MobileNet-V1 (https://arxiv.org/abs/1704.04861)
- MobileNet-V2 (https://arxiv.org/abs/1801.04381)
- MobileNet-V3 (https://arxiv.org/abs/1905.02244) -- native PyTorch model trained better than paper spec
- ChamNet (https://arxiv.org/abs/1812.08934) -- specific arch details hard to find, currently an educated guess
- FBNet-C (https://arxiv.org/abs/1812.03443) -- TODO A/B variants
- Single-Path NAS (https://arxiv.org/abs/1904.02877) -- pixel1 variant
I originally implemented and trained some these models with code here, this repository contains just the GenEfficientNet models, validation, and associated ONNX/Caffe2 export code.
Pretrained
I've managed to train several of the models to accuracies close to or above the originating papers and official impl. My training code is here: https://github.com/rwightman/pytorch-image-models
| Model | Prec@1 (Err) | Prec@5 (Err) | Param#(M) | MAdds(M) | Image Scaling | Resolution | Crop |
|---|---|---|---|---|---|---|---|
| mixnet_l | 78.976 (21.024 | 94.184 (5.816) | 7.33 | TBD | bicubic | 224 | 0.875 |
| efficientnet_b2 | 79.668 (20.332) | 94.634 (5.366) | 9.1 | 1003 | bicubic | 260 | 0.890 |
| efficientnet_b1 | 78.692 (21.308) | 94.086 (5.914) | 7.8 | 694 | bicubic | 240 | 0.882 |
| mixnet_m | 77.256 (22.744) | 93.418 (6.582) | 5.01 | 353 | bicubic | 224 | 0.875 |
| efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.3 | 390 | bicubic | 224 | 0.875 |
| mixnet_s | 75.988 (24.012) | 92.794 (7.206) | 4.13 | TBD | bicubic | 224 | 0.875 |
| mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5 | 219 | bicubic | 224 | 0.875 |
| mnasnet_a1 | 75.448 (24.552) | 92.604 (7.396) | 3.9 | 312 | bicubic | 224 | 0.875 |
| fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6 | 385 | bilinear | 224 | 0.875 |
| mnasnet_b1 | 74.658 (25.342) | 92.114 (7.886) | 4.4 | 315 | bicubic | 224 | 0.875 |
| spnasnet_100 | 74.084 (25.916) | 91.818 (8.182) | 4.4 | TBV | bilinear | 224 | 0.875 |
More pretrained models to come...
Ported Weights
The weights ported from Tensorflow checkpoints for the EfficientNet models do pretty much match accuracy in Tensorflow once a SAME convolution padding equivalent is added, and the same crop factors, image scaling, etc (see table) are used via cmd line args.
Ex, to run validation for tf_efficientnet_b5:
python validate.py /path/to/imagenet/validation/ --model tf_efficientnet_b5 -b 64 --img-size 456 --crop-pct 0.934 --interpolation bicubic
Enabling the Tensorflow preprocessing pipeline with --tf-preprocessing at validation time will improve these scores by 0.1-0.5% as it's closer to what these models were trained with.
Ex, to run validation w/ TF preprocessing for tf_efficientnet_b5:
python validate.py /path/to/imagenet/validation/ --model tf_efficientnet_b5 -b 64 --img-size 456 --tf-preprocessing
EdgeTPU models use different normalization consts. Use Inception style 0.5, 0.5, 0.5 for mean and std.
| Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size | Crop |
|---|---|---|---|---|---|---|
| tf_efficientnet_b7 *tfp | 84.480 (15.520) | 96.870 (3.130) | 66.35 | bicubic | 600 | N/A |
| tf_efficientnet_b7 | 84.420 (15.580) | 96.906 (3.094) | 66.35 | bicubic | 600 | 0.949 |
| tf_efficientnet_b6 *tfp | 84.140 (15.860) | 96.852 (3.148) | 43.04 | bicubic | 528 | N/A |
| tf_efficientnet_b6 | 84.110 (15.890) | 96.886 (3.114) | 43.04 | bicubic | 528 | 0.942 |
| tf_efficientnet_b5 *tfp | 83.694 (16.306) | 96.696 (3.304) | 30.39 | bicubic | 456 | N/A |
| tf_efficientnet_b5 | 83.688 (16.312) | 96.714 (3.286) | 30.39 | bicubic | 456 | 0.934 |
| tf_efficientnet_b4 | 83.022 (16.978) | 96.300 (3.700) | 19.34 | bicubic | 380 | 0.922 |
| tf_efficientnet_b4 *tfp | 82.948 (17.052) | 96.308 (3.692) | 19.34 | bicubic | 380 | N/A |
| tf_efficientnet_b3 *tfp | 81.576 (18.424) | 95.662 (4.338) | 12.23 | bicubic | 300 | N/A |
| tf_efficientnet_b3 | 81.636 (18.364) | 95.718 (4.282) | 12.23 | bicubic | 300 | 0.903 |
| tf_efficientnet_el | 80.534 (19.466) | 95.190 (4.810) | 10.59 | bicubic | 300 | 0.903 |
| tf_efficientnet_el *tfp | 80.476 (19.524) | 95.200 (4.800) | 10.59 | bicubic | 300 | N/A |
| tf_efficientnet_b2 *tfp | 80.188 (19.812) | 94.974 (5.026) | 9.11 | bicubic | 260 | N/A |
| tf_efficientnet_b2 | 80.086 (19.914) | 94.908 (5.092) | 9.11 | bicubic | 260 | 0.890 |
| tf_efficientnet_b1 *tfp | 79.172 (20.828) | 94.450 (5.550) | 7.79 | bicubic | 240 N/A | |
| tf_efficientnet_em *tfp | 78.958 (21.042) | 94.458 (5.542) | 6.90 | bicubic | 240 | N/A |
| tf_mixnet_l *tfp | 78.846 (21.154) | 94.212 (5.788) | 7.33 | bilinear | 224 | N/A |
| tf_efficientnet_b1 | 78.826 (21.174) | 94.198 (5.802) | 7.79 | bicubic | 240 | 0.88 |
| tf_mixnet_l | 78.770 (21.230) | 94.004 (5.996) | 7.33 | bicubic | 224 | 0.875 |
| tf_efficientnet_em | 78.742 (21.258) | 94.332 (5.668) | 6.90 | bicubic | 240 | 0.875 |
| tf_efficientnet_es *tfp | 77.616 (22.384) | 93.750 (6.250) | 5.44 | bicubic | 224 | N/A |
| tf_efficientnet_es | 77.264 (22.736) | 93.600 (6.400) | 5.44 | bicubic | 224 | N/A |
| tf_efficientnet_b0 *tfp | 77.258 (22.742) | 93.478 (6.522) | 5.29 | bicubic | 224 | N/A |
| tf_mixnet_m *tfp | 77.072 (22.928) | 93.368 (6.632) | 5.01 | bilinear | 224 | N/A |
| tf_mixnet_m | 76.950 (23.050) | 93.156 (6.844) | 5.01 | bicubic | 224 | 0.875 |
| tf_efficientnet_b0 | 76.848 (23.152) | 93.228 (6.772) | 5.29 | bicubic | 224 | 0.875 |
| tf_mixnet_s *tfp | 75.800 (24.200) | 92.788 (7.212) | 4.13 | bilinear | 224 | N/A |
| tf_mixnet_s | 75.648 (24.352) | 92.636 (7.364) | 4.13 | bicubic | 224 | 0.875 |
*tfp models validated with tf-preprocessing pipeline
Google tf and tflite weights ported from official Tensorflow repositories
- https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
- https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
PyTorch Hub
Models can be accessed via the PyTorch Hub API
>>> torch.hub.list('rwightman/gen-efficientnet-pytorch')
['efficientnet_b0', ...]
>>> model = torch.hub.load('rwightman/gen-efficientnet-pytorch', 'efficientnet_b0', pretrained=True)
>>> model.eval()
>>> output = model(torch.randn(1,3,224,224))
Exporting
Scripts to export models to ONNX and then to Caffe2 are included, along with a Caffe2 script to verify.
As an example, to export the MobileNet-V3 pretrained model and then run an Imagenet validation:
python onnx_export.py --model mobilenetv3_100 ./mobilenetv3_100.onnx
python onnx_to_caffe.py ./mobilenetv3_100.onnx --c2-prefix mobilenetv3
python caffe2_validate.py /imagenet/validation/ --c2-init ./mobilenetv3.init.pb --c2-predict ./mobilenetv3.predict.pb --interpolation bicubic
NOTE the TF ported weights with the 'SAME' conv padding activated cannot be exported to ONNX unless _EXPORTABLE flag in conv2d_helpers.py is set to True.