I-JEPA

This model was released on 2023-01-19 and added to Hugging Face Transformers on 2024-12-05.

I-JEPA

I-JEPA is a self-supervised learning method that learns semantic image representations by predicting parts of an image from other parts of the image. It compares the abstract representations of the image (rather than pixel level comparisons), which avoids the typical pitfalls of data augmentation bias and pixel-level details that don’t capture semantic meaning.

You can find the original I-JEPA checkpoints under the AI at Meta organization.

Tip

This model was contributed by jmtzt.

Click on the I-JEPA models in the right sidebar for more examples of how to apply I-JEPA to different image representation and classification tasks.

The example below demonstrates how to extract image features with Pipeline or the AutoModel class.

import torch
from transformers import pipeline
feature_extractor = pipeline(
    task="image-feature-extraction",
    model="facebook/ijepa_vith14_1k",
    device=0,
    dtype=torch.bfloat16
)
features = feature_extractor("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg", return_tensors=True)

print(f"Feature shape: {features.shape}")

import requests
import torch
from PIL import Image
from torch.nn.functional import cosine_similarity
from transformers import AutoModel, AutoProcessor

url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
image_1 = Image.open(requests.get(url_1, stream=True).raw)
image_2 = Image.open(requests.get(url_2, stream=True).raw)

processor = AutoProcessor.from_pretrained("facebook/ijepa_vith14_1k")
model = AutoModel.from_pretrained("facebook/ijepa_vith14_1k", dtype="auto", attn_implementation="sdpa")


def infer(image):
    inputs = processor(image, return_tensors="pt")
    outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1)


embed_1 = infer(image_1)
embed_2 = infer(image_2)

similarity = cosine_similarity(embed_1, embed_2)
print(similarity)

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends. The example below uses bitsandbytes to only quantize the weights to 4-bits.

import torch
from transformers import BitsAndBytesConfig, AutoModel, AutoProcessor
from datasets import load_dataset

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
image_1 = Image.open(requests.get(url_1, stream=True).raw)
image_2 = Image.open(requests.get(url_2, stream=True).raw)

processor = AutoProcessor.from_pretrained("facebook/ijepa_vitg16_22k")
model = AutoModel.from_pretrained("facebook/ijepa_vitg16_22k", quantization_config=quantization_config, dtype="auto", attn_implementation="sdpa")


def infer(image):
    inputs = processor(image, return_tensors="pt")
    outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1)


embed_1 = infer(image_1)
embed_2 = infer(image_2)

similarity = cosine_similarity(embed_1, embed_2)
print(similarity)

IJepaConfig

[[autodoc]] IJepaConfig

IJepaModel

[[autodoc]] IJepaModel - forward

IJepaForImageClassification

[[autodoc]] IJepaForImageClassification - forward