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Want to validate some hparams or figure out what 
Want to validate some hparams or figure out what 
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`timm`
timm
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 model to use before commiting to download or training with a large dataset? Try mini-imagenet: 
 model to use before commiting to download or training with a large dataset? Try mini-imagenet: 
resource
https://huggingface.co/datasets/timm/mini-imagenet
dataset
timm/mini-imagenet
https://huggingface.co/datasets/timm/mini-imagenet
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I had this sitting on my drive and forgot where I pulled it together from. It's 100 classes of imagenet, 50k train and 10k val  images (from ImageNet-1k train set), and 5k test images (from ImageNet-1k val set). 7.4GB instead of > 100GB for the full ImageNet-1k. This ver is not reduced resolution like some other 'mini' versions. Super easy to use with timm train/val scripts, checkout the dataset card.
I had this sitting on my drive and forgot where I pulled it together from. It's 100 classes of imagenet, 50k train and 10k val  images (from ImageNet-1k train set), and 5k test images (from ImageNet-1k val set). 7.4GB instead of > 100GB for the full ImageNet-1k. This ver is not reduced resolution like some other 'mini' versions. Super easy to use with timm train/val scripts, checkout the dataset card.
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I often check fine-tuning with even smaller datasets like:
I often check fine-tuning with even smaller datasets like:
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 * 
 * 
resource
https://huggingface.co/datasets/timm/resisc45
dataset
timm/resisc45
https://huggingface.co/datasets/timm/resisc45
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 * 
 * 
resource
https://huggingface.co/datasets/timm/oxford-iiit-pet
dataset
timm/oxford-iiit-pet
https://huggingface.co/datasets/timm/oxford-iiit-pet
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But those are a bit small to train any modest size model w/o starting from pretrained weights. 
But those are a bit small to train any modest size model w/o starting from pretrained weights. 
Want to validate some hparams or figure out what `timm` model to use before commiting to download or training with a large dataset? Try mini-imagenet: https://huggingface.co/datasets/timm/mini-imagenet 

I had this sitting on my drive and forgot where I pulled it together from. It's 100 classes of imagenet, 50k train and 10k val  images (from ImageNet-1k train set), and 5k test images (from ImageNet-1k val set). 7.4GB instead of > 100GB for the full ImageNet-1k. This ver is not reduced resolution like some other 'mini' versions. Super easy to use with timm train/val scripts, checkout the dataset card.

I often check fine-tuning with even smaller datasets like:
 * https://huggingface.co/datasets/timm/resisc45
 * https://huggingface.co/datasets/timm/oxford-iiit-pet
But those are a bit small to train any modest size model w/o starting from pretrained weights. 
🚀
bryant1410 John6666 byoussef davanstrien clem