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Add weights for my MixNet-XL creation, include README updates for EdgeTPU models

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pull/30/head
Ross Wightman 5 years ago
parent 51a2375b0c
commit 73fbd97ed4
2 changed files with 13 additions and 1 deletions
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8
README.md
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@ -33,6 +33,7 @@ I've included a few of my favourite models, but this is not an exhaustive collec
* DPN-68, DPN-68b, DPN-92, DPN-98, DPN-131, DPN-107
* Generic EfficientNet (from my standalone [GenMobileNet](https://github.com/rwightman/genmobilenet-pytorch)) - A generic model that implements many of the efficient models that utilize similar DepthwiseSeparable and InvertedResidual blocks
* 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)
@ -71,6 +72,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
|Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling | Image Size |
|---|---|---|---|---|---|
| mixnet_xl | 80.120 (19.880) | 95.022 (4.978) | 11.90M | bicubic | 224 |
| efficientnet_b2 | 79.760 (20.240) | 94.714 (5.286) | 9.11M | bicubic | 260 |
| resnext50d_32x4d | 79.674 (20.326) | 94.868 (5.132) | 25.1M | bicubic | 224 |
| mixnet_l | 78.976 (21.024 | 94.184 (5.816) | 7.33M | bicubic | 224 |
@ -111,6 +113,8 @@ I've leveraged the training scripts in this repository to train a few of the mod
| gluon_seresnext101_32x4d | 80.902 (19.098) | 95.294 (4.706) | 48.96 | bicubic | 224 | |
| gluon_seresnext101_64x4d | 80.890 (19.110) | 95.304 (4.696) | 88.23 | bicubic | 224 | |
| gluon_resnext101_64x4d | 80.602 (19.398) | 94.994 (5.006) | 83.46 | bicubic | 224 | |
| tf_efficientnet_el | 80.534 (19.466) | 95.190 (4.810) | 10.59 | bicubic | 300 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) |
| tf_efficientnet_el *tfp | 80.476 (19.524) | 95.200 (4.800) | 10.59 | bicubic | 300 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) |
| gluon_resnet152_v1d | 80.470 (19.530) | 95.206 (4.794) | 60.21 | bicubic | 224 | |
| gluon_resnet101_v1d | 80.424 (19.576) | 95.020 (4.980) | 44.57 | bicubic | 224 | |
| gluon_resnext101_32x4d | 80.334 (19.666) | 94.926 (5.074) | 44.18 | bicubic | 224 | |
@ -126,15 +130,19 @@ I've leveraged the training scripts in this repository to train a few of the mod
| gluon_resnet101_v1b | 79.304 (20.696) | 94.524 (5.476) | 44.55 | bicubic | 224 | |
| tf_efficientnet_b1 *tfp | 79.172 (20.828) | 94.450 (5.550) | 7.79 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
| gluon_resnet50_v1d | 79.074 (20.926) | 94.476 (5.524) | 25.58 | bicubic | 224 | |
| tf_efficientnet_em *tfp | 78.958 (21.042) | 94.458 (5.542) | 6.90 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) |
| tf_mixnet_l *tfp | 78.846 (21.154) | 94.212 (5.788) | 7.33 | bilinear | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
| tf_efficientnet_b1 | 78.826 (21.174) | 94.198 (5.802) | 7.79 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
| gluon_inception_v3 | 78.804 (21.196) | 94.380 (5.620) | 27.16M | bicubic | 299 | [MxNet Gluon](https://gluon-cv.mxnet.io/model_zoo/classification.html) |
| tf_mixnet_l | 78.770 (21.230) | 94.004 (5.996) | 7.33 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
| tf_efficientnet_em | 78.742 (21.258) | 94.332 (5.668) | 6.90 | bicubic | 240 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) |
| gluon_resnet50_v1s | 78.712 (21.288) | 94.242 (5.758) | 25.68 | bicubic | 224 | |
| gluon_resnet50_v1c | 78.010 (21.990) | 93.988 (6.012) | 25.58 | bicubic | 224 | |
| tf_inception_v3 | 77.856 (22.144) | 93.644 (6.356) | 27.16M | bicubic | 299 | [Tensorflow Slim](https://github.com/tensorflow/models/tree/master/research/slim) |
| tf_efficientnet_es *tfp | 77.616 (22.384) | 93.750 (6.250) | 5.44 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) |
| gluon_resnet50_v1b | 77.578 (22.422) | 93.718 (6.282) | 25.56 | bicubic | 224 | |
| adv_inception_v3 | 77.576 (22.424) | 93.724 (6.276) | 27.16M | bicubic | 299 | [Tensorflow Adv models](https://github.com/tensorflow/models/tree/master/research/adv_imagenet_models) |
| tf_efficientnet_es | 77.264 (22.736) | 93.600 (6.400) | 5.44 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu) |
| tf_efficientnet_b0 *tfp | 77.258 (22.742) | 93.478 (6.522) | 5.29 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
| tf_mixnet_m *tfp | 77.072 (22.928) | 93.368 (6.632) | 5.01 | bilinear | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |
| tf_mixnet_m | 76.950 (23.050) | 93.156 (6.844) | 5.01 | bicubic | 224 | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet/mixnet) |

6
timm/models/gen_efficientnet.py
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@ -138,7 +138,8 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth'),
'mixnet_l': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth'),
'mixnet_xl': _cfg(),
'mixnet_xl': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl-ac5fbe8d.pth'),
'mixnet_xxl': _cfg(),
'tf_mixnet_s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'),
@ -1919,6 +1920,7 @@ def mixnet_l(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
@register_model
def mixnet_xl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Creates a MixNet Extra-Large model.
Not a paper spec, experimental def by RW w/ depth scaling.
"""
default_cfg = default_cfgs['mixnet_xl']
#kwargs['drop_connect_rate'] = 0.2
@ -1933,8 +1935,10 @@ def mixnet_xl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
@register_model
def mixnet_xxl(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
"""Creates a MixNet Double Extra Large model.
Not a paper spec, experimental def by RW w/ depth scaling.
"""
default_cfg = default_cfgs['mixnet_xxl']
# kwargs['drop_connect_rate'] = 0.2
model = _gen_mixnet_m(
channel_multiplier=2.4, depth_multiplier=1.3, num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg

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