Let's gooo! We're happy to introduce two new official Space Templates in collaboration with @posit_pbc
: Build and share your R and Python Shiny apps directly on the Hub!
@huggingface
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Posit Shiny Space Templates Launch on Hub
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Hugging Face Fellows Network Showcases Open Source ML Contributions
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Hugging Face Fellows is a network of exceptional people from different backgrounds contributing to open-source machine learning Read what our Fellows have been up to here https://
huggingface2.notion.site/Hugging-Face-F
ellow-Highlights-April-7-1a3f6715ca7848a18d52f52ac00501d5
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Meta releases SAM vision model checkpoints on Hugging Face
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Link to model checkpoints: base model: https://
huggingface.co/facebook/sam-v
it-base
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large model: https://
huggingface.co/facebook/sam-v
it-large
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huge model: https://
huggingface.co/facebook/sam-v
it-huge
… Use them right now on transformers main branch, by installing the library from source -
Using AutoModel.from_pretrained for Model Deployment
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… and here is what is needed to use the model! Just call AutoModel.from_pretrained
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Automatic Mask Generation Pipeline with Few Lines Code
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All these features with few lines of code!
Here is what is needed now to run the automatic mask generation pipeline -
Segment Anything Model: Bounding boxes to segmentation masks
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Provide a set of bounding boxes or a set 2D points and predict the segmentation mask of the object of interest! Check the detailed notebook here: https://
github.com/huggingface/no
tebooks/blob/main/examples/segment_anything.ipynb
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Automatic mask generation pipeline released by Hugging Face
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Automatic mask generation pipeline! With one line of code predict automatically the segmentation masks of a given image (similar as the examples above) Check out the example notebook: https://
github.com/huggingface/no
tebooks/blob/main/examples/automatic_mask_generation.ipynb
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SAM Meta Segmentation Model Now Available in Transformers
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SAM, the groundbreaking segmentation model from @Meta is now in available in Transformers!
What does this mean? 1. One line of code to load it, one line to run it
2. Efficient batching support to generate multiple masks
3. pipeline support for easier usage More details: -
SAM Model Documentation and Features Overview
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You can first read more about the model, and learn how to use it on our documentation page: https://
huggingface.co/docs/transform
ers/main/en/model_doc/sam
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Let's check all the features we support below! -
Beijing AI Community Meetup: JAX Diffusers Sprint with Google Cloud
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Our FIRST community meetup in Beijing is ON LIVE
With @googlecloud
, we empower the AI enthusiasts to develop their project with JAX Diffusers You can still join our ongoing sprint: https://
github.com/huggingface/co
mmunity-events/tree/main/jax-controlnet-sprint
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