---
license: apache-2.0
---
# Model Details
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](https://ai.meta.com/research/publications/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.
| Scale | Tower | Params | Width | Depth | MLP | Heads | CLIP Dim | Resolution | Patch Size | Text Context Length |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **B** | Vision | 0.09B | 768 | 12 | 3072 | 12 | 1024 | 224 | 16 | 32 |
| | Text | 0.31B | 1024 | 24 | 4096 | 16 | 1024 | 224 | 16 | 32 |
| **L** | Vision | 0.32B | 1024 | 24 | 4096 | 16 | 1024 | 336 | 14 | 32 |
| | Text | 0.31B | 1024 | 24 | 4096 | 16 | 1024 | 336 | 14 | 32 |
| **G** | Vision | 1.88B | 1536 | 50 | 8960 | 16 | 1280 | 448 | 14 | 72 |
| | Text | 0.47B | 1280 | 24 | 5120 | 20 | 1280 | 448 | 14 | 72 |
# How to use
## PE codebase
We provide the pretraining code in https://github.com/facebookresearch/perception_models
```shell
git clone https://github.com/facebookresearch/perception_models.git
cd perception_models
conda create --name occhi-env python=3.12
conda activate occhi-env
# Install PyTorch
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 xformers --index-url https://download.pytorch.org/whl/cu124
# We use torchcodec for decoding videos into PyTorch tensors
conda install ffmpeg -c conda-forge
pip install torchcodec==0.1 --index-url=https://download.pytorch.org/whl/cu124
pip install -e .
```
## Image and Textg Feature extraction with a Trained Model :robot:
```python
import torch
from occhi.vision_encoder.factory import create_model_and_transforms, get_tokenizer
from PIL import Image
model_name = 'PEv1-B16-224 '
pretrained='PATH_TO_PE_Core_B16_224'
model, _, preprocess = create_model_and_transforms(
model_name,
pretrained=pretrained,
)
model = model.cuda()
tokenizer = get_tokenizer(model_name)
image = preprocess(Image.open("docs/cat.png")).unsqueeze(0).cuda()
text = tokenizer(["a diagram", "a dog", "a cat"]).cuda()
with torch.no_grad(), torch.autocast("cuda"):
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs) # prints: [[0.0, 0.0, 1.0]]
```
You can find more details in the GitHub repo.
# Evaluation
We evaluate the pretrained PE models on Zero-shot Common Sense Reasoning tasks
Here is the table in Markdown format:
## Zero-Shot Image Results
## Zero-Shot Video Results
# Citation
If you find our code useful for your research, please consider citing:
@article{PE,
title={Perception Encoder: The best visual embeddings are not at the output of the network},
author={},
journal={arXiv:xxx.xxxxx},
year={2025}
}