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*This model was released on 2023-06-23 and added to Hugging Face Transformers on 2025-06-17 and contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).*

# LightGlue

[LightGlue](https://huggingface.co/papers/2306.13643) is a deep neural network that learns to match local features across images. It revisits multiple design decisions of SuperGlue and derives simple but effective improvements. Cumulatively, these improvements make LightGlue more efficient - in terms of both memory and computation, more accurate, and much easier to train. Similar to [SuperGlue](https://huggingface.co/magic-leap-community/superglue_outdoor), this model consists of matching two sets of local features extracted from two images, with the goal of being faster than SuperGlue. Paired with the [SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and estimate the pose between them.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="keypoint-matching", model="ETH-CVG/lightglue_superpoint", dtype="auto")
url_0 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg"
url_1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg"

pipeline([url_0, url_1], threshold=0.9)
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForKeypointMatching

url_image1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg"
image1 = Image.open(requests.get(url_image1, stream=True).raw)
url_image2 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg"
image2 = Image.open(requests.get(url_image2, stream=True).raw)

images = [image1, image2]

processor = AutoImageProcessor.from_pretrained("ETH-CVG/lightglue_superpoint")
model = AutoModelForKeypointMatching.from_pretrained("ETH-CVG/lightglue_superpoint", dtype="auto")
inputs = processor(images, return_tensors="pt")

with torch.inference_mode():
    outputs = model(**inputs)

image_sizes = [[(image.height, image.width) for image in images]]
processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)

for i, output in enumerate(processed_outputs):
    print(f"For the image pair {i}")
    for keypoint0, keypoint1, matching_score in zip(
            output["keypoints0"], output["keypoints1"], output["matching_scores"]
    ):
        print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")

visualized_images = processor.visualize_keypoint_matching(images, processed_outputs)
```

</hfoption>
</hfoptions>

## LightGlueConfig

[[autodoc]] LightGlueConfig

## LightGlueImageProcessor

[[autodoc]] LightGlueImageProcessor

- preprocess
- post_process_keypoint_matching
- visualize_keypoint_matching

## LightGlueForKeypointMatching

[[autodoc]] LightGlueForKeypointMatching

- forward
