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- timm PE-Core-bigG-14-448 · Hugging Face
PE core G is the main checkpoint, with L and B models distilled from it All PE core models use an attention pooling block with 8 heads on top of the vision tower The L and B models additionally have a class token for global aggregation See the paper for more details
- PE-Core-bigG-14-448. json - GitHub
An open source implementation of CLIP Contribute to mlfoundations open_clip development by creating an account on GitHub
- PE-Core-G14-448 · Models
By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks
- open_clip_inference - Rust - Docs. rs
If you need a model that hasn’t been converted to ONNX on HuggingFace yet, you can easily convert any open_clip compatible model yourself, using pull_onnx py from this repo
- GitHub - facebookresearch perception_models: State-of-the-art Image . . .
PE core: a CLIP model excels in vision-language tasks such as zero-shot image and video classification and video retrieval PE lang: a LLM-aligned PE that powers PLM to compete at the forefront of multimodal LLM benchmarks
- ONNX | Home
ONNX is an open format built to represent machine learning models ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers
- open_clip_inference — ML AI statistics in Rust Lib. rs
I've uploaded the following ONNX Clip Embedding models to HuggingFace To get an idea of the speed quality tradeoff for these models, I've benchmarked them, and put them alongside the ImageNet zero-shot accuracy score
- README. md · timm PE-Core-bigG-14-448 at main - Hugging Face
By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks
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