Searching for better (Full) ImageNet ViT Baselines
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Ross Wightman
rwightman
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posted
an
update
14 days ago
Post
1110
The
Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
* test_vit ( timm/test_vit.r160_in1k)
* test_efficientnet ( timm/test_efficientnet.r160_in1k)
* test_byobnet ( timm/test_byobnet.r160_in1k, a mix of resnet, darknet, effnet/regnet like blocks)
They are < 0.5M params, insanely fast and originally intended for unit testing w/ real weights. They have awful ImageNet top-1, it's rare to have anyone bother to train a model this small on ImageNet (the classifier is roughly 30-70% of the param count!). However, they are FAST on very limited hadware and you can fine-tune them well on small data. Could be the model you're looking for?
timm leaderboard
timm/leaderboard has been updated with the ability to select different hardware benchmark sets: RTX4090, RTX3090, two different CPUs along with some NCHW / NHWC layout and torch.compile (dynamo) variations. Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
* test_vit ( timm/test_vit.r160_in1k)
* test_efficientnet ( timm/test_efficientnet.r160_in1k)
* test_byobnet ( timm/test_byobnet.r160_in1k, a mix of resnet, darknet, effnet/regnet like blocks)
They are < 0.5M params, insanely fast and originally intended for unit testing w/ real weights. They have awful ImageNet top-1, it's rare to have anyone bother to train a model this small on ImageNet (the classifier is roughly 30-70% of the param count!). However, they are FAST on very limited hadware and you can fine-tune them well on small data. Could be the model you're looking for?
posted
an
update
28 days ago
Post
2018
The latest timm validation & test set results are now viewable by a leaderboard space:
timm/leaderboard
As of yesterday, I updated all of the results for ImageNet , ImageNet-ReaL, ImageNet-V2, ImageNet-R, ImageNet-A, and Sketch sets. The csv files can be found in the GH repo https://github.com/huggingface/pytorch-image-models/tree/main/results
Unfortunately the latest benchmark csv files are not yet up to date, there are some gaps in dataset results vs throughput/flop numbers impact the plots.
h/t to @MohamedRashad for making the first timm leaderboard.
As of yesterday, I updated all of the results for ImageNet , ImageNet-ReaL, ImageNet-V2, ImageNet-R, ImageNet-A, and Sketch sets. The csv files can be found in the GH repo https://github.com/huggingface/pytorch-image-models/tree/main/results
Unfortunately the latest benchmark csv files are not yet up to date, there are some gaps in dataset results vs throughput/flop numbers impact the plots.
h/t to @MohamedRashad for making the first timm leaderboard.
- 1 reply
posted
an
update
about 2 months ago
Post
1958
I can't resist an opportunity to update an old baseline. Read a new article on my latest look at improving MobileNet-V1 and EfficientNet-B0 baselines.
https://huggingface.co/blog/rwightman/mobilenet-baselines
timm/mobilenetv1_100.ra4_e3600_r224_in1k
timm/efficientnet_b0.ra4_e3600_r224_in1k
https://huggingface.co/blog/rwightman/mobilenet-baselines
timm/mobilenetv1_100.ra4_e3600_r224_in1k
timm/efficientnet_b0.ra4_e3600_r224_in1k
posted
an
update
3 months ago
Post
2420
MobileNetV4 weights are now in timm! So far these are the only weights for these models as the offiicial Tensorflow impl remains weightless.
Guided by paper hparams with a few tweaks, I've managed to match or beat the paper results training the medium models. I'm still working on large and improving the small result. They appear to be solid models for on-device use.
timm/mobilenetv4-pretrained-weights-6669c22cda4db4244def9637
MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
Guided by paper hparams with a few tweaks, I've managed to match or beat the paper results training the medium models. I'm still working on large and improving the small result. They appear to be solid models for on-device use.
timm/mobilenetv4-pretrained-weights-6669c22cda4db4244def9637
MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
- 1 reply
posted
an
update
4 months ago
Post
1468
Aiming to keep
Paper: MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
timm (https://github.com/huggingface/pytorch-image-models) the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: https://huggingface.co/blog/rwightman/mobilenetv4Paper: MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)
posted
an
update
4 months ago
Post
1850
timm 1.0 is finally out. The big feature that I wanted to complete before doing this? Having the unified feature map extraciton interface (features_only=True) supporting almost all models (97%) 🎉 See docs at https://huggingface.co/docs/timm/en/feature_extractionAlso in this release, the new set of SBB (searching for better baselins) ViT models, covering new architectures and hparam exploration between tiny and base. See timm/searching-for-better-vit-baselines-663eb74f64f847d2f35a9c19
I also snuck in image-tower loading for PaliGemma (via jax weights on Hub) google/paligemma-release-6643a9ffbf57de2ae0448dda
Excited this is finally out the door! Huzzah