The 
text
The 
timm
`timm`
inline_code
 leaderboard 
text
 leaderboard 
https://huggingface.co/spaces/timm/leaderboard
timm/leaderboard
space
resource
https://huggingface.co/spaces/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. 
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 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. 
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Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
text
Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
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* test_vit (
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* test_vit (
https://huggingface.co/timm/test_vit.r160_in1k
timm/test_vit.r160_in1k
model
resource
https://huggingface.co/timm/test_vit.r160_in1k
)
text
)
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* test_efficientnet (
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* test_efficientnet (
https://huggingface.co/timm/test_efficientnet.r160_in1k
timm/test_efficientnet.r160_in1k
model
resource
https://huggingface.co/timm/test_efficientnet.r160_in1k
)
text
)
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* test_byobnet (
text
* test_byobnet (
https://huggingface.co/timm/test_byobnet.r160_in1k
timm/test_byobnet.r160_in1k
model
resource
https://huggingface.co/timm/test_byobnet.r160_in1k
, a mix of resnet, darknet, effnet/regnet like blocks)
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, a mix of resnet, darknet, effnet/regnet like blocks)
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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?
text
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?
The `timm` leaderboard https://huggingface.co/spaces/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 (https://huggingface.co/timm/test_vit.r160_in1k)
* test_efficientnet (https://huggingface.co/timm/test_efficientnet.r160_in1k)
* test_byobnet (https://huggingface.co/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?
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