Trick or ResNet Treat
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3
Ross Wightman
rwightman
AI & ML interests
Computer vision, transfer learning, semi/self supervised learning, robotics.
Recent Activity
New activity
3 days ago
timm/ViT-B-16-SigLIP-i18n-256:Are the languages that are supported documented anywhere?
updated
a model
5 days ago
timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
Reacted to
jeffboudier's
post
with 🤗
8 days ago
New - add your bluesky account to your HF profile:
https://huggingface.co/settings/profile
Is the grass greener, the sky bluer? Will try and figure it out at https://bsky.app/profile/jeffboudier.bsky.social
By the way, HF people starter pack https://bsky.app/starter-pack/huggingface.bsky.social/3laz5x7naiz22
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1137
I'm currently on a push to expand the scope of image based datasets on the Hub. There's certainly a lot already, but for anyone who's looked closely, there's not a whole lot of standardization. I am to fix that, datasets under the https://huggingface.co/timm and https://huggingface.co/pixparse orgs will serve as canonical examples for various task / modality combinations and be useable without fuss in libraries like
I just uploaded the first multi-label dataset that I'll support with
Next up object detection & segmentation! I've got an annotation spec sorted out, a lot of datasets ready to rip, and yeah that means
timm, OpenCLIP, and hopefully more.I just uploaded the first multi-label dataset that I'll support with
timm scripts soon:
timm/plant-pathology-2021 Next up object detection & segmentation! I've got an annotation spec sorted out, a lot of datasets ready to rip, and yeah that means
timm support for object detection, eventually segmentation, is finally under development :OPost
1034
Want to validate some hparams or figure out what
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:
* timm/resisc45
* timm/oxford-iiit-pet
But those are a bit small to train any modest size model w/o starting from pretrained weights.
timm model to use before commiting to download or training with a large dataset? Try mini-imagenet:
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:
* timm/resisc45
* timm/oxford-iiit-pet
But those are a bit small to train any modest size model w/o starting from pretrained weights.
Collections
1
Fine-tune experiments for various `timm` models on the INaturalist 2021 Challenge dataset (https://github.com/visipedia/inat_comp/tree/master/2021)
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timm/eva02_large_patch14_clip_336.merged2b_ft_inat21
Image Classification • Updated • 44 • 11 -
timm/vit_large_patch14_clip_336.datacompxl_ft_augreg_inat21
Image Classification • Updated • 6 • 1 -
timm/vit_large_patch14_clip_336.laion2b_ft_augreg_inat21
Image Classification • Updated • 12 -
timm/vit_large_patch14_clip_336.datacompxl_ft_inat21
Image Classification • Updated • 10 • 1
