5259dbcbb4
Update README.md with latest changes
2019-11-29 17:58:33 -08:00
3bef524f9c
Finish with HRNet, weights and models updated. Improve consistency in model classifier/global pool treatment.
2019-11-29 17:56:36 -08:00
3ceeedc441
Merge pull request #53 from rwightman/condconvs_and_features
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Major model merge (EfficientNet-CondConv, EfficientNet-AdvProp, TF MobileNetV3, HRNet, more)
2019-11-28 18:05:32 -08:00
7b3c235ccc
Update sotabench with new models
2019-11-28 17:56:59 -08:00
6ca0828166
Update EfficientNet comments, MobileNetV3 non-TF create fns, fix factory arg checks, bump PyTorch version req to 1.2
2019-11-28 17:43:00 -08:00
eccbadca74
Update EfficientNet comments
2019-11-28 17:11:53 -08:00
902d32fb16
Renamed gen_efficientnet.py -> efficientnet.py
2019-11-28 17:04:35 -08:00
5a0a8de7e3
ResNet updates:
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* remove redundant GluonResNet model/blocks and use the code in ResNet for Gluon weights
* change SEModules back to using AdaptiveAvgPool instead of mean, PyTorch issue long fixed
2019-11-28 17:04:35 -08:00
3b8f63084d
Clean checkpoint renames pth w/ SHA hash in a torch hub compatible way
2019-11-28 17:04:35 -08:00
a39cc43374
Bring EfficientNet and MobileNetV3 up to date with my gen-efficientnet repo
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* Split MobileNetV3 and EfficientNet model files and put builder and blocks in own files (getting too large)
* Finalize CondConv EfficientNet variant
* Add the AdvProp weights files and B8 EfficientNet model
* Refine the feature extraction module for EfficientNet and MobileNetV3
2019-11-28 17:04:35 -08:00
ad93347548
Initial HRNet classification model commit
2019-11-28 17:00:52 -08:00
2393708650
Missed stashing of out_indices in model
2019-11-28 17:00:52 -08:00
35e8f0c5e7
Fixup a few comments, add PyTorch version aware Flatten and finish as_sequential for GenEfficientNet
2019-11-28 17:00:52 -08:00
7ac6db4543
Missed activations.py
2019-11-28 17:00:52 -08:00
506df0e3d0
Add CondConv support for EfficientNet into WIP for GenEfficientNet Feature extraction setup
2019-11-28 17:00:52 -08:00
db04677c94
Merge pull request #52 from rwightman/randaugment
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RandAugment and more
2019-11-22 14:05:29 -08:00
576d360f20
Bring in JIT version of optimized swish activation from gen_efficientnet as default (while working on feature extraction functionality here).
2019-11-22 13:57:45 -08:00
1f39d15f15
Allow float decay epochs arg for training, works out with step lr math
2019-11-22 13:28:58 -08:00
7b83e67f77
Pass drop connect arg through to EfficientNet models
2019-11-22 13:27:43 -08:00
31453b039e
Update Auto/RandAugment comments, README, more.
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* Add a weighted choice option for RandAugment
* Adjust magnitude noise/std naming, config
2019-11-22 13:24:52 -08:00
4243f076f1
Adding RandAugment to AutoAugment impl, some tweaks to AA included
2019-11-21 21:14:33 -08:00
4748c6dff2
Fix non-prefetch variant of Mixup. Fixes #50
2019-11-02 17:42:55 -07:00
0d58c50fb1
Add TF RandAug weights for B5/B7 EfficientNet models.
2019-10-30 16:49:17 -07:00
62105ed4a0
Better differentiate sotabench WSL, SSL, and SWSL models via model_desc
2019-10-20 13:17:25 -07:00
c099374771
Map pretrained checkpoint to cpu to avoid issue with some pretrained checkpoints still having CUDA tensors. Fixes #42
2019-10-19 17:27:46 -07:00
b93fcf0708
Add Facebook Research Semi-Supervised and Semi-Weakly Supervised ResNet model weights.
2019-10-19 17:05:37 -07:00
a9eb484835
Add memory efficient Swish impl
2019-10-19 14:48:30 -07:00
187ecbafbe
Add support for loading args from yaml file (and saving them with each experiment)
2019-09-09 17:56:45 -07:00
d3ba34ee7e
Fix Mobilenet V3 model name for sotabench. Minor res2net cleanup.
2019-09-05 15:47:56 -07:00
b5a8bb52fd
Silly typos.
2019-09-04 21:36:58 -07:00
7dc5d7a997
sotabench fail
2019-09-04 19:15:36 -07:00
96364fc336
Merge pull request #35 from rwightman/res2net_dla
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Add Res2net and DLA models. Update sotabench.
2019-09-04 17:41:50 -07:00
2680ad14bb
Add Res2Net and DLA to README
2019-09-04 17:38:59 -07:00
adbf770f16
Add Res2Net and DLA models w/ pretrained weights. Update sotabench.
2019-09-04 17:06:42 -07:00
7fd0857bfe
Merge pull request #33 from rwightman/autoaugment
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Add AutoAugment ImageNet policies for training
2019-09-01 22:10:03 -07:00
4002c0d4ce
Fix AutoAugment abs translate calc
2019-09-01 22:07:45 -07:00
c06274e5a2
Add note on random selection of magnitude value
2019-09-01 20:32:26 -07:00
b750b76f67
More AutoAugment work. Ready to roll...
2019-09-01 16:55:42 -07:00
25d2088d9e
Working on auto-augment
2019-08-31 23:09:48 -07:00
aff194f42c
Merge pull request #32 from rwightman/opt
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More optimizer work
2019-08-29 15:26:15 -07:00
64966f61f7
Add Nvidia's NovogGrad impl from Jasper (cleaner/faster than current) and Apex Fused optimizers
2019-08-29 15:21:38 -07:00
3d9c8a6489
Add support for new AMP checkpointing support w/ amp.state_dict
2019-08-29 15:19:18 -07:00
ba3c97c3ad
Some Lookahead cleanup and fixes
2019-08-29 15:14:35 -07:00
5c6da1c55a
Experiment with sotabench model_description field.
2019-08-28 21:34:39 -07:00
e9d2ec4d8e
Merge pull request #31 from rwightman/opt
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Optimizers and more
2019-08-28 00:20:39 -07:00
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
2019-08-28 00:14:10 -07:00
81875d52a6
Update sotabench model list, add Mean-Max pooling DPN variants, disable download progress
2019-08-27 00:07:32 -07:00
f37e633e9b
Merge remote-tracking branch 'origin/re-exp' into opt
2019-08-26 14:29:23 -07:00
b06dce8d71
Bump version for next push to pypi
2019-08-25 22:32:12 -07:00
32a0ca03c8
Update csv results files
2019-08-25 22:27:14 -07:00
Ross Wightman
Ross Wightman