text
The 
The 
inline_code
`timm`
timm
text
 leaderboard 
 leaderboard 
resource
https://huggingface.co/spaces/timm/leaderboard
space
timm/leaderboard
https://huggingface.co/spaces/timm/leaderboard
text
 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. 
 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. 
new_line
new_line
text
Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
Also worth pointing out, there are three rather newish 'test' models that you'll see at the top of any samples/sec comparison:
new_line
text
* test_vit (
* test_vit (
resource
https://huggingface.co/timm/test_vit.r160_in1k
model
timm/test_vit.r160_in1k
https://huggingface.co/timm/test_vit.r160_in1k
text
)
)
new_line
text
* test_efficientnet (
* test_efficientnet (
resource
https://huggingface.co/timm/test_efficientnet.r160_in1k
model
timm/test_efficientnet.r160_in1k
https://huggingface.co/timm/test_efficientnet.r160_in1k
text
)
)
new_line
text
* test_byobnet (
* test_byobnet (
resource
https://huggingface.co/timm/test_byobnet.r160_in1k
model
timm/test_byobnet.r160_in1k
https://huggingface.co/timm/test_byobnet.r160_in1k
text
, a mix of resnet, darknet, effnet/regnet like blocks)
, a mix of resnet, darknet, effnet/regnet like blocks)
new_line
new_line
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?
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?
❤️
clem MohamedRashad John6666 bryant1410
🔥
de-Rodrigo
👍
maxiw