d86dbe45c2
Update README.md and few more comments
4 years ago
e4de077021
Add first 'Normalizer Free' models. nf_regnet_b1 79.3 @ 288x288 test, and nf_resnet50 80.3 @ 256x256 test (80.68 @ 288x288).
4 years ago
ca9b078ac7
Update README.md and docs. Version bumped to 0.4.3
4 years ago
aaa715b1e9
Update README.md
4 years ago
a39c3ee216
Merge branch 'master' into eca-weights
4 years ago
e9d6fe293c
Update README for new weights. Version 0.4.2
4 years ago
3a7aa95f7e
Update README.md
4 years ago
2c988c3b6e
Update README.md for NF-nets, bump version to 0.4.1 for merge
4 years ago
745bc5f723
Mention 21k ViT weights in README.md
4 years ago
587780e56b
Update README.md and bump version to 0.4.0
4 years ago
58ccf43150
Add BiT references and knowledge distill links to readme/docs
4 years ago
19816fe226
Add citation info
4 years ago
1d01c2b68c
Update README.md
4 years ago
7a75b8d033
Update README.md
4 years ago
b1f1228a41
Add ResNet101D, 152D, and 200D weights, remove meh 66d model
4 years ago
51d74d91da
Update README.md
4 years ago
a7f6126b92
Update README.md
4 years ago
46f15443be
Update README.md and docs in prep for 0.3.0 PyPi release.
4 years ago
736f209e7d
Update vision transformers to be compatible with official code. Port official ViT weights from jax impl.
4 years ago
27a93e9de7
Improve test crop for ViT models. Small now 77.85, added base weights at 79.35 top-1.
4 years ago
be53107e8a
Update README, ensure vit excluded from all tests (not ready)
4 years ago
c40384f5bd
Add ResNet weights. 80.5 (top-1) ResNet-50-D, 77.1 ResNet-34-D, 72.7 ResNet-18-D.
4 years ago
9ce42d5c5a
Update README.md
4 years ago
0729dbe865
Update README.md
4 years ago
33f8a1bf36
Updated README, add wide_resnet50_2 and seresnext50_32x4d weights
4 years ago
6d158adfe9
Update README.md
4 years ago
fa26f6c1cf
Update README.md
4 years ago
f614df7921
Bump version to 0.2.1 and update README
4 years ago
6e9d6172c8
A few typos and missed updates in changelog
4 years ago
1696499ce5
Bump version to 0.2.0, ready to roll (I think)
4 years ago
e62758cf4f
More documentation updates, fix a typo
4 years ago
5e333b899f
README / doc tweaks
4 years ago
e3f58fc90c
Continuing README and documentation updates
4 years ago
e3c11a36dc
Start updating README and docs
4 years ago
328339a086
Add ESE-VoVNet-19-DW weights
4 years ago
f225ae8e59
Update README with model results and attribution. Make scheduler factory bit more robust to arg differences, add noise to plateau lr and fix min/max.
4 years ago
39f27c1add
Almost ready to merge, vovnet typo, version bump, readme addition
5 years ago
208e7912f7
Missed one of the abalation model entrypoints, update README
5 years ago
f4cdc2ac31
Add ResNeSt models
5 years ago
353a79aeba
Update README.md
5 years ago
a4d20a1cb8
Update README.md
5 years ago
c99a5abed4
Merge pull request #115 from rwightman/mobilenetv2-experiment
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MobileNet-V2 experiments
5 years ago
e34074b4da
Add final weights for MobileNet-V2 experiments
5 years ago
5a16c533ff
Add better resnext50_32x4d weights trained by andravin
5 years ago
3406e582cf
Add EfficientNet-Lite results, update README
5 years ago
c16f25ced2
Add MobileNetV3 Large weights, results, update README and sotabench for merge
5 years ago
f1860ef3a5
Update README.md, check in hubconf experiment (fully dynamic)
5 years ago
b9496995d8
Add L2-475 PyTorch preprocessing result, update sotabench for new weights
5 years ago
ba15ca47e8
Add ported EfficientNet-L2, B0-B7 NoisyStudent weights from TF TPU
5 years ago
5eb0e363a6
Update README.md
5 years ago
5c4991a088
Add PyTorch trained EfficientNet-ES weights from Andrew Lavin
5 years ago
4808b3c32f
Bump version for PyPi update, fix few out of date README items/mistakes, add README updates for TF EfficientNet-B8 (RandAugment)
5 years ago
b18c19901e
Update README.md
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Typo
5 years ago
12dbc74742
New ResNet50 JSD + RandAugment weights
5 years ago
e39aae56b4
Update README.md
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Fix typo
5 years ago
7a17ee9c3b
Update README.md
5 years ago
2a88412413
Update README.md
5 years ago
cfa951bceb
Update README.md
5 years ago
7622015258
Update README.md
5 years ago
ec0dd4053a
Add updated RandAugment trained EfficientNet-B0 trained weights from @michaelklachko
5 years ago
a7fe891a13
Update README.md
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Update readme with SelecSLS details.
5 years ago
0554b7942c
Update README.md
5 years ago
53f578e16f
Update README with B3 training details
5 years ago
acc3ed2b8c
Add EfficientNet-B3 weights, trained from scratch with RA.
5 years ago
1f4498f217
Add ResNet deep tiered stem and model weights for seresnext26t_32x4d and seresnext26d_32x4d
5 years ago
73b78459dc
Add update RandAugment MixNet-XL weights
5 years ago
5d7af97981
Update README.md
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Update latest training hparam/command line with correct RandAugment config string (`noise` -> `mstd`)
5 years ago
3129bdb2c1
Update README.md
5 years ago
ff421e5e09
New PyTorch trained EfficientNet-B2 weights with my RandAugment impl
5 years ago
5259dbcbb4
Update README.md with latest changes
5 years ago
31453b039e
Update Auto/RandAugment comments, README, more.
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* Add a weighted choice option for RandAugment
* Adjust magnitude noise/std naming, config
5 years ago
0d58c50fb1
Add TF RandAug weights for B5/B7 EfficientNet models.
5 years ago
2680ad14bb
Add Res2Net and DLA to README
5 years ago
fac58f609a
Add RAdam, NovoGrad, Lookahead, and AdamW optimizers, a few ResNet tweaks and scheduler factory tweak.
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* Add some of the trendy new optimizers. Decent results but not clearly better than the standards.
* Can create a None scheduler for constant LR
* ResNet defaults to zero_init of last BN in residual
* add resnet50d config
5 years ago
73fbd97ed4
Add weights for my MixNet-XL creation, include README updates for EdgeTPU models
5 years ago
3b4868f6dc
A few more additions to Gluon Xception models to match interface of others.
5 years ago
9b35818811
Update README.md
5 years ago
4fe2da558c
Add MixNet Small and Large PyTorch native weights (no same padding)
5 years ago
15e66863b1
Update README.md
5 years ago
e879cf52fa
Update validation scores for new TF EfficientNet weights.
5 years ago
b35050a144
Update README.md
5 years ago
857f33015a
Add native PyTorch weights for MixNet-Medium with no SAME padding necessary. Remove unused block of code.
5 years ago
dfa9298b4e
Add MixNet ( https://arxiv.org/abs/1907.09595 ) with pretrained weights converted from Tensorflow impl
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* refactor 'same' convolution and add helper to use MixedConv2d when needed
* improve performance of 'same' padding for cases that can be handled statically
* add support for extra exp, pw, and dw kernel specs with grouping support to decoder/string defs for MixNet
* shuffle some args for a bit more consistency, a little less clutter overall in gen_efficientnet.py
5 years ago
d97510fd4a
Add new results csv and update README with 3 new ResNet weight results
5 years ago
5684c6af32
Update README.md
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Add blurb about results csv
5 years ago
a6b2f6eca5
Update README, bump version
5 years ago
c3287aafb3
Slight improvement in EfficientNet-B2 native PyTorch weights
5 years ago
b8762cc67d
Model updates. Add my best ResNet50 weights top-1=78.47. Add some other torchvision weights.
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* Remove some models that don't exist as pretrained an likely never will (se)resnext152
* Add some torchvision weights as tv_ for models that I have added better weights for
* Add wide resnet recently added to torchvision along with resnext101-32x8d
* Add functionality to model registry to allow filtering on pretrained weight presence
5 years ago
13c19e213d
Add native PyTorch EfficientNet B1 and B2 weights. Not quite where I want them, but hitting the brick wall and moving on to other projects...
5 years ago
8512436436
Add instagram pretrained ResNeXt models from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ , update README
5 years ago
6fc886acaf
Remove all prints, change most to logging calls, tweak alignment of batch logs, improve setup.py
5 years ago
1019414fd2
Add ported Tensorflow EfficientNet B4/B5 weights
5 years ago
c6b8259ad6
Update README.md
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Add blurb about python environment
6 years ago
f3134973b5
Add the MnasNet-B1 variant weights, add A1/B1 model names as in the stand-alone repo, remove a bit of unused code
6 years ago
2060e433c0
Add native PyTorch weights for SE-MnasNet aka MnasNet-A1
6 years ago
7d17394bdc
Add native PyTorch weights for EfficientNet-B0 w/ top-1 > 76.9
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* also add pooling details to default cfg for efficiennets so testtimepool wrapper works
6 years ago
105d5702d7
Update README.md
6 years ago
6688d0669f
PyTorch trained MobileNetV3 weights that match/best paper. Update fbnetc weights to improved copy.
6 years ago
b32361c9f8
Correct name
6 years ago
99122aac1c
Replace ResNet-34 default weights with a great result from my experiments.
6 years ago
Ross Wightman