mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Add more maxxvit weights includ ConvNeXt conv block based experiments.
This commit is contained in:
Ross Wightman
parent
145c4d8e46
commit
9914f744dc
15
README.md
15
README.md
@ -21,6 +21,21 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before
|
||||
|
||||
## What's New
|
||||
|
||||
### Oct 10, 2022
|
||||
* More weights in `maxxvit` series, incl first ConvNeXt block based `coatnext` and `maxxvit` experiments:
|
||||
* `coatnext_nano_rw_224` - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm)
|
||||
* `maxxvit_nano_rw_256` - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN)
|
||||
* `maxvit_rmlp_small_rw_224` - 84.5 @ 224, 85.1 @ 320 (G)
|
||||
* `maxxvit_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
|
||||
* `coatnet_rmlp_2_rw_224` - 84.6 @ 224, 85 @ 320 (T)
|
||||
|
||||
### Sept 23, 2022
|
||||
* LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier)
|
||||
* vit_base_patch32_224_clip_laion2b
|
||||
* vit_large_patch14_224_clip_laion2b
|
||||
* vit_huge_patch14_224_clip_laion2b
|
||||
* vit_giant_patch14_224_clip_laion2b
|
||||
|
||||
### Sept 7, 2022
|
||||
* Hugging Face [`timm` docs](https://huggingface.co/docs/hub/timm) home now exists, look for more here in the future
|
||||
* Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2
|
||||
|
||||
@ -95,10 +95,13 @@ default_cfgs = {
|
||||
'coatnet_rmlp_0_rw_224': _cfg(url=''),
|
||||
'coatnet_rmlp_1_rw_224': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_1_rw_224_sw-9051e6c3.pth'),
|
||||
'coatnet_rmlp_2_rw_224': _cfg(url=''),
|
||||
'coatnet_rmlp_2_rw_224': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnet_rmlp_2_rw_224_sw-5ccfac55.pth'),
|
||||
'coatnet_rmlp_3_rw_224': _cfg(url=''),
|
||||
'coatnet_nano_cc_224': _cfg(url=''),
|
||||
'coatnext_nano_rw_224': _cfg(url=''),
|
||||
'coatnext_nano_rw_224': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/coatnext_nano_rw_224_ad-22cb71c2.pth',
|
||||
crop_pct=0.9),
|
||||
|
||||
# Trying to be like the CoAtNet paper configs
|
||||
'coatnet_0_224': _cfg(url=''),
|
||||
@ -128,16 +131,22 @@ default_cfgs = {
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_tiny_rw_256_sw-bbef0ff5.pth',
|
||||
input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
'maxvit_rmlp_small_rw_224': _cfg(
|
||||
url=''),
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_small_rw_224_sw-6ef0ae4f.pth',
|
||||
crop_pct=0.9,
|
||||
),
|
||||
'maxvit_rmlp_small_rw_256': _cfg(
|
||||
url='',
|
||||
input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
|
||||
'maxvit_tiny_pm_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
|
||||
'maxxvit_nano_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
'maxxvit_tiny_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
'maxxvit_small_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
'maxxvit_rmlp_nano_rw_256': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxxvit_rmlp_nano_rw_256_sw-0325d459.pth',
|
||||
input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
'maxxvit_rmlp_tiny_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
'maxxvit_rmlp_small_rw_256': _cfg(
|
||||
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxxvit_rmlp_small_rw_256_sw-37e217ff.pth',
|
||||
input_size=(3, 256, 256), pool_size=(8, 8)),
|
||||
|
||||
# Trying to be like the MaxViT paper configs
|
||||
'maxvit_tiny_224': _cfg(url=''),
|
||||
@ -334,13 +343,14 @@ def _next_cfg(
|
||||
rel_pos_dim=512,
|
||||
):
|
||||
# For experimental models with convnext instead of mbconv
|
||||
init_values = to_2tuple(init_values)
|
||||
return dict(
|
||||
conv_cfg=MaxxVitConvCfg(
|
||||
block_type='convnext',
|
||||
stride_mode=stride_mode,
|
||||
pool_type=pool_type,
|
||||
expand_output=False,
|
||||
init_values=init_values,
|
||||
init_values=init_values[0],
|
||||
norm_layer=conv_norm_layer,
|
||||
norm_layer_cl=conv_norm_layer_cl,
|
||||
),
|
||||
@ -348,7 +358,7 @@ def _next_cfg(
|
||||
expand_first=False,
|
||||
pool_type=pool_type,
|
||||
window_size=window_size,
|
||||
init_values=init_values,
|
||||
init_values=init_values[1],
|
||||
norm_layer=transformer_norm_layer,
|
||||
norm_layer_cl=transformer_norm_layer_cl,
|
||||
rel_pos_type=rel_pos_type,
|
||||
@ -497,7 +507,10 @@ model_cfgs = dict(
|
||||
depths=(3, 4, 6, 3),
|
||||
stem_width=(32, 64),
|
||||
weight_init='normal',
|
||||
**_next_cfg(),
|
||||
**_next_cfg(
|
||||
rel_pos_type='bias',
|
||||
init_values=(1e-5, None)
|
||||
),
|
||||
),
|
||||
|
||||
# Trying to be like the CoAtNet paper configs
|
||||
@ -612,7 +625,7 @@ model_cfgs = dict(
|
||||
**_rw_max_cfg(),
|
||||
),
|
||||
|
||||
maxxvit_nano_rw_256=MaxxVitCfg(
|
||||
maxxvit_rmlp_nano_rw_256=MaxxVitCfg(
|
||||
embed_dim=(64, 128, 256, 512),
|
||||
depths=(1, 2, 3, 1),
|
||||
block_type=('M',) * 4,
|
||||
@ -620,14 +633,14 @@ model_cfgs = dict(
|
||||
weight_init='normal',
|
||||
**_next_cfg(),
|
||||
),
|
||||
maxxvit_tiny_rw_256=MaxxVitCfg(
|
||||
maxxvit_rmlp_tiny_rw_256=MaxxVitCfg(
|
||||
embed_dim=(64, 128, 256, 512),
|
||||
depths=(2, 2, 5, 2),
|
||||
block_type=('M',) * 4,
|
||||
stem_width=(32, 64),
|
||||
**_next_cfg(),
|
||||
),
|
||||
maxxvit_small_rw_256=MaxxVitCfg(
|
||||
maxxvit_rmlp_small_rw_256=MaxxVitCfg(
|
||||
embed_dim=(96, 192, 384, 768),
|
||||
depths=(2, 2, 5, 2),
|
||||
block_type=('M',) * 4,
|
||||
@ -1861,18 +1874,18 @@ def maxvit_tiny_pm_256(pretrained=False, **kwargs):
|
||||
|
||||
|
||||
@register_model
|
||||
def maxxvit_nano_rw_256(pretrained=False, **kwargs):
|
||||
return _create_maxxvit('maxxvit_nano_rw_256', pretrained=pretrained, **kwargs)
|
||||
def maxxvit_rmlp_nano_rw_256(pretrained=False, **kwargs):
|
||||
return _create_maxxvit('maxxvit_rmlp_nano_rw_256', pretrained=pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def maxxvit_tiny_rw_256(pretrained=False, **kwargs):
|
||||
return _create_maxxvit('maxxvit_tiny_rw_256', pretrained=pretrained, **kwargs)
|
||||
def maxxvit_rmlp_tiny_rw_256(pretrained=False, **kwargs):
|
||||
return _create_maxxvit('maxxvit_rmlp_tiny_rw_256', pretrained=pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def maxxvit_small_rw_256(pretrained=False, **kwargs):
|
||||
return _create_maxxvit('maxxvit_small_rw_256', pretrained=pretrained, **kwargs)
|
||||
def maxxvit_rmlp_small_rw_256(pretrained=False, **kwargs):
|
||||
return _create_maxxvit('maxxvit_rmlp_small_rw_256', pretrained=pretrained, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user