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ChristophSchuhmann  updated a dataset 1 day ago
laion/LAION-Audio-300M
felfri  authored a paper 19 days ago
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps
PSaiml  authored a paper 19 days ago
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps
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ChristophSchuhmann 
updated a dataset 1 day ago
rwightman 
posted an update 2 days ago
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New timm 1.0.13 and OpenCLIP 2.30.0 releases to start the year. Both modest but worthwhile updates.

timm added a number of new model weights, supporting loading of:
* PaliGemma2 encoders (ported from google/paligemma-2-release-67500e1e1dbfdd4dee27ba48)
* AIMv-2 encoders (ported from apple/aimv2-6720fe1558d94c7805f7688c)

A few higher resolution 384x384 ConvNeXt-Nano ImageNet-12k pretrain & finetunes. See other changes here: https://github.com/huggingface/pytorch-image-models/releases/tag/v1.0.13

And support added in both OpenCLIP and timm for two CLIP models that were missed. The DFN L/14 is 🔥
* DFN CLIP L/14 w/ 39B samples seen - apple/DFN2B-CLIP-ViT-L-14-39B, timm/vit_large_patch14_clip_224.dfn2b_s39b
* MetaCLIP H/14 (altogether) - timm/vit_huge_patch14_clip_224.metaclip_altogether

And last, ~70-80 models that were relying on timm remapping from OpenCLIP got their own timm hub instances to allow use with the upcoming Transformers TimmWrapperModel
felfri 
authored a paper 19 days ago
PSaiml 
authored a paper 19 days ago
sheryc 
authored a paper 24 days ago
AmeyaPrabhu 
authored a paper 26 days ago
rwightman 
posted an update about 1 month ago
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1380
There's a new timm release, v 1.0.12, with a focus on optimizers. The optimizer factory has been refactored, there's now a timm.optim.list_optimizers() and new way to register optimizers and their attributes. As always you can use an timm optimizer like a torch one, just replace torch.optim with timm.optim

New optimizers include:
* AdafactorBigVision - adfactorbv
* ADOPT - adopt / adoptw (decoupled decay)
* MARS - mars
* LaProp - laprop
* Cautious Optimizers - a modification to all of the above, prefix with c as well as cadamw, cnadamw, csgdw, clamb, crmsproptf

I shared some caution comparisons in this model repo: rwightman/timm-optim-caution

For details, references, see the code: https://github.com/huggingface/pytorch-image-models/tree/main/timm/optim

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rwightman 
posted an update about 2 months ago
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1338
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 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 :O
rwightman 
posted an update about 2 months ago
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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: 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.
rwightman 
posted an update about 2 months ago
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New MobileNetV4 weights were uploaded a few days ago -- more ImageNet-12k training at 384x384 for the speedy 'Conv Medium' models.

There are 3 weight variants here for those who like to tinker. On my hold-out eval they are ordered as below, not that different, but the Adopt 180 epochs closer to AdamW 250 than to AdamW 180.
* AdamW for 250 epochs - timm/mobilenetv4_conv_medium.e250_r384_in12k
* Adopt for 180 epochs - timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
* AdamW for 180 epochs - timm/mobilenetv4_conv_medium.e180_r384_in12k

This was by request as a user reported impressive results using the 'Conv Large' ImagNet-12k pretrains as object detection backbones. ImageNet-1k fine-tunes are pending, the weights do behave differently with the 180 vs 250 epochs and the Adopt vs AdamW optimizer.

Alignment-Lab-AI 
posted an update 2 months ago
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remember boys and girls, always keep all your data, its never a waste of time!
rwightman 
posted an update 3 months ago
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A new timm release (1.0.11) is out now. A also wrote an article on one of the included models: https://huggingface.co./blog/rwightman/mambaout

Featured in the release are:
* The MambaOut model, a cheeky arch inspired by SSM but without the SSM part, a ConvNeXt with gating.
* Several timm trained MambaOut variations with arch tweaks and ImageNet-12k pretrain to verify scaling, supplement ported weights.
* The smallest MobileNetV4, a 0.5x width scaled Conv-Small.
* Two impressive MobileNetV3 Large models outperforming all previous, using MNV4 Small recipe.
* 'Zepto,' a new compact ConvNeXt variant even smaller than the previous Atto, 2.2M params, RMSNorm, and solid results for its size.
* Newly ported SigLIP SO400M/16 ViT multi-lingual weights, the largest i18n weights, prevous was B/16.
* Two ImageNet-1k fine-tuned SigLIP SO400M models at 378x378
* InternViT 300M weight port. A really solid ViT encoder distilled from OpenGVLab 6B VL model encoder.
* An assortment of very small, sub 1M param pretrained test models to improve library unit tests and serve low-resource applications.
JJitsev 
authored 8 papers 3 months ago