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