PE Core S 16 384 EU AI Act Compliance Profile

Model card for PE-Core-S-16-384

This is an OpenCLIP (image + text) remaped version of the the original

[📃 Tech Report] [📂 PE Github (original weights)] [📂 OpenCLIP Github (these weights)]

Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "Perception Encoder: The best visual embeddings are not at the output of the network".

Model Developer: Meta

Model Overview: Perception Encoder (PE) is a family of large-scale vision encoder models with state-of-the-art performance on a large variety of vision tasks. 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. PE unlocks the ability for large-scale contrastive pretraining to transfer to downstream tasks with alignment tuning to capitalize on those general features.

Perception Encoder: Core

PE core is our base model trained with our robust image pretraining schedule and finetuned on the data generated by our synthetic video data engine.

Model Configurations

PE core curently comes in 3 sizes. PE core G is the main checkpoint, with L and B models distilled from it.

| Scale | Tower | Params | Width | Depth | MLP | Heads | CLIP Dim | Resolution / Context Len | |:-----:|:------:|:------:|:-----:|:-----:|:----:|:-----:|:-----

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