9613c76844
Add mobilenet edgetpu defs for exp, add ol mobilenet v1 back for completeness / comparison
2024-06-13 17:33:04 -07:00
22de845add
Prepping for final MobileCLIP weight locations ( #2199 )
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* Prepping for final MobileCLIP weight locations
* Update weight locations to coreml-projects
* Update mobileclip weight locations with final apple org location
2024-06-13 16:55:49 -07:00
575978ba55
Add mnv4_conv_large 384x384 weight location
2024-06-13 12:58:04 -07:00
e42e453128
Fix mmnv4 conv_large weight link, reorder mnv4 pretrained cfg for proper precedence
2024-06-12 11:16:49 -07:00
7b0a5321cb
Merge pull request #2198 from huggingface/openai_clip_resnet
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Mapping OpenAI CLIP Modified ResNet weights -> ByobNet.
2024-06-12 09:33:30 -07:00
57adc1acc8
Fix rotary embed version of attn pool. Bit of cleanup/naming
2024-06-11 23:49:17 -07:00
cdc7bcea69
Make 2d attention pool modules compatible with head interface. Use attention pool in CLIP ResNets as head. Make separate set of GAP models w/ avg pool instead of attn pool.
2024-06-11 21:32:07 -07:00
c63da1405c
Pretrained cfg name mismatch
2024-06-11 21:16:54 -07:00
88efca1be2
First set of MobileNetV4 weights trained in timm
2024-06-11 18:53:01 -07:00
30ffa152de
Fix load of larger ResNet CLIP models, experimenting with making AttentionPool *the* head, seems to fine-tune better, one less layer.
2024-06-10 12:07:14 -07:00
5e9ff5798f
Adding pos embed resize fns to FX autowrap exceptions
2024-06-10 12:06:47 -07:00
f0fb471b26
Remove separate ConvNormActAa class, merge with ConvNormAct
2024-06-10 12:05:35 -07:00
5efa15b2a2
Mapping OpenAI CLIP Modified ResNet weights -> ByobNet. Improve AttentionPool2d layers. Fix #1731
2024-06-09 16:54:48 -07:00
7702d9afa1
ViTamin in_chans !=3 weight load fix
2024-06-07 20:39:23 -07:00
5ee06760dc
Fix classifier input dim for mnv3 after last changes
2024-06-07 13:53:13 -07:00
a5a2ad2e48
Fix consistency, testing for forward_head w/ pre_logits, reset_classifier, models with pre_logits size != unpooled feature size
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* add test that model supports forward_head(x, pre_logits=True)
* add head_hidden_size attr to all models and set differently from num_features attr when head has hidden layers
* test forward_features() feat dim == model.num_features and pre_logits feat dim == self.head_hidden_size
* more consistency in reset_classifier signature, add typing
* asserts in some heads where pooling cannot be disabled
Fix #2194
2024-06-07 13:53:00 -07:00
4535a5412a
Change default serialization for push_to_hf_hub to 'both'
2024-06-07 13:40:31 -07:00
7ccb10ebff
Disable efficient_builder debug flag
2024-06-06 21:50:27 -07:00
ad026e6e33
Fix in_chans switching on create
2024-06-06 17:56:14 -07:00
fc1b66a51d
Fix first conv name for mci vit-b
2024-06-06 13:42:26 -07:00
88a1006e02
checkpoint filter fns with consistent name, add mobileclip-b pretrained cfgs
2024-06-06 12:38:52 -07:00
7d4ada6d16
Update ViTamin model defs
2024-06-06 09:16:43 -07:00
cc8a03daac
Add ConvStem and MobileCLIP hybrid model for B variant. Add full norm disable support to ConvNormAct layers
2024-06-06 09:15:27 -07:00
3c9d8e5b33
Merge remote-tracking branch 'origin/efficientnet_x' into fastvit_mobileclip
2024-06-05 17:35:15 -07:00
5756a81c55
Merge remote-tracking branch 'origin/Beckschen-vitamin' into fastvit_mobileclip
2024-06-05 15:20:54 -07:00
58591a97f7
Enable features_only properly
2024-06-04 16:57:16 -07:00
1b66ec7cf3
Fixup ViTamin, add hub weight reference
2024-06-03 17:14:03 -07:00
b2c0aeb0ec
Merge branch 'main' of https://github.com/Beckschen/pytorch-image-models into Beckschen-vitamin
2024-06-02 14:16:30 -07:00
7f96538052
Add missing lkc act for mobileclip fastvits
2024-05-31 11:59:51 -07:00
a503639bcc
Add mobileclip fastvit model defs, support extra SE. Add forward_intermediates API to fastvit
2024-05-30 10:17:38 -07:00
5fa6efa158
Add anti-aliasing support to mobilenetv3 and efficientnet family models. Update MobileNetV4 model defs, resolutions. Fix #599
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* create_aa helper function centralized for all timm uses (resnet, convbnact helper)
* allow BlurPool w/ pre-defined channels (expand)
* mobilenetv4 UIB block using ConvNormAct layers for improved clarity, esp with AA added
* improve more mobilenetv3 and efficientnet related type annotations
2024-05-27 22:06:22 -07:00
5dce710101
Add vit_little in12k + in12k-ft-in1k weights
2024-05-27 14:56:03 -07:00
3c0283f9ef
Fix reparameterize for NextViT. Fix #2187
2024-05-27 14:48:58 -07:00
4ff7c25766
Pass layer_scale_init_value to Mnv3Features module
2024-05-24 16:44:50 -07:00
a12b72b5c4
Fix missing head_norm arg pop for feature model
2024-05-24 15:50:34 -07:00
7fe96e7a92
More MobileNet-v4 fixes
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* missed final norm after post pooling 1x1 PW head conv
* improve repr of model by flipping a few modules to None when not used, nn.Sequential for MultiQueryAttention query/key/value/output
* allow layer scaling to be enabled/disabled at model variant level, conv variants don't use it
2024-05-24 15:09:29 -07:00
28d76a97db
Mixed up kernel size for last blocks in mnv4-conv-small
2024-05-24 11:50:42 -07:00
0c6a69e7ef
Add comments to MNV4 model defs with block variants
2024-05-23 15:54:05 -07:00
cb33956b20
Fix some mistakes in mnv4 model defs
2024-05-23 14:24:32 -07:00
cee79dada0
Merge remote-tracking branch 'origin/main' into efficientnet_x
2024-05-23 11:01:39 -07:00
6a8bb03330
Initial MobileNetV4 pass
2024-05-23 10:49:18 -07:00
e748805be3
Add regex matching support to AttentionExtract. Add return_dict support to graph extractors and use returned output in AttentionExtractor
2024-05-22 14:33:39 -07:00
84cb225ecb
Add in12k + 12k_ft_in1k vit_medium weights
2024-05-20 15:52:46 -07:00
7a2ad6bce1
Add link to model weights on Hugging Face
2024-05-17 06:51:35 -04:00
530fb49e7e
Add link to model weights on Hugging Face
2024-05-17 06:48:59 -04:00
cd0e7b11ff
Merge pull request #2180 from yvonwin/main
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Remove a duplicate function in mobilenetv3.py
2024-05-15 07:54:17 -07:00
83aee5c28c
Add explicit GAP (avg pool) variants of other SigLIP models.
2024-05-15 07:53:19 -07:00
58f2f79b04
Remove a duplicate function in mobilenetv3.py: `_gen_lcnet` is repeated in mobilenetv3.py.Remove the duplicate code.
2024-05-15 17:59:34 +08:00
7b3b11b63f
Support loading of paligemma weights into GAP variants of SigLIP ViT. Minor tweak to npz loading for packed transformer weights.
2024-05-14 15:44:37 -07:00
df304ffbf2
the dataclass init needs to use the default factory pattern, according to Ross
2024-05-14 15:10:05 -04:00
Ross Wightman
Ross Wightman
Beckschen
yvonwin