mirrors
/
pytorch-image-models
mirror of https://github.com/huggingface/pytorch-image-models.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Add native PyTorch weights for EfficientNet-B0 w/ top-1 > 76.9 

* also add pooling details to default cfg for efficiennets so testtimepool wrapper works
pull/13/head
Ross Wightman 2019-06-10 13:11:36 -07:00
parent 105d5702d7
commit 7d17394bdc
3 changed files with 16 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -67,6 +67,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
|---|---|---|---|---| |---|---|---|---|---|
| resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic | | resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic |
| seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic | | seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
| efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic |
| mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic | | mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic |
| fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear | | fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear |
| resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear | | resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear |

22
models/gen_efficientnet.py
View File

@ -74,23 +74,29 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth'), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth'),
'spnasnet_100': _cfg( 'spnasnet_100': _cfg(
url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'), url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'),
'efficientnet_b0': _cfg(url=''), 'efficientnet_b0': _cfg(
'efficientnet_b1': _cfg(url='', input_size=(3, 240, 240)), url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth',
'efficientnet_b2': _cfg(url='', input_size=(3, 260, 260)), interpolation='bicubic'),
'efficientnet_b3': _cfg(url='', input_size=(3, 300, 300)), 'efficientnet_b1': _cfg(
'efficientnet_b4': _cfg(url='', input_size=(3, 380, 380)), url='', input_size=(3, 240, 240), pool_size=(8, 8)),
'efficientnet_b2': _cfg(
url='', input_size=(3, 260, 260), pool_size=(9, 9)),
'efficientnet_b3': _cfg(
url='', input_size=(3, 300, 300), pool_size=(10, 10)),
'efficientnet_b4': _cfg(
url='', input_size=(3, 380, 380), pool_size=(12, 12)),
'tf_efficientnet_b0': _cfg( 'tf_efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0-0af12548.pth',
input_size=(3, 224, 224), interpolation='bicubic'), input_size=(3, 224, 224), interpolation='bicubic'),
'tf_efficientnet_b1': _cfg( 'tf_efficientnet_b1': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1-5c1377c4.pth',
input_size=(3, 240, 240), interpolation='bicubic', crop_pct=0.882), input_size=(3, 240, 240), pool_size=(8, 8), interpolation='bicubic', crop_pct=0.882),
'tf_efficientnet_b2': _cfg( 'tf_efficientnet_b2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2-e393ef04.pth',
input_size=(3, 260, 260), interpolation='bicubic', crop_pct=0.890), input_size=(3, 260, 260), pool_size=(9, 9), interpolation='bicubic', crop_pct=0.890),
'tf_efficientnet_b3': _cfg( 'tf_efficientnet_b3': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth', url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3-e3bd6955.pth',
input_size=(3, 300, 300), interpolation='bicubic', crop_pct=0.904), input_size=(3, 300, 300), pool_size=(10, 10), interpolation='bicubic', crop_pct=0.904),
} }
_DEBUG = False _DEBUG = False

2
models/test_time_pool.py
View File

@ -31,7 +31,7 @@ def apply_test_time_pool(model, config, args):
if not args.no_test_pool and \ if not args.no_test_pool and \
config['input_size'][-1] > model.default_cfg['input_size'][-1] and \ config['input_size'][-1] > model.default_cfg['input_size'][-1] and \
config['input_size'][-2] > model.default_cfg['input_size'][-2]: config['input_size'][-2] > model.default_cfg['input_size'][-2]:
print('Target input size (%s) > pretrained default (%s), using test time pooling' % print('Target input size %s > pretrained default %s, using test time pooling' %
(str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:]))) (str(config['input_size'][-2:]), str(model.default_cfg['input_size'][-2:])))
model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size']) model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
test_time_pool = True test_time_pool = True