Updated model card (#1)

- Updated model card (655ca09461baa74bf9cf412a1e9a5e6b91ec9927)


Co-authored-by: Steven Bucaille <stevenbucaille@users.noreply.huggingface.co>

README.md

@@ -3,4 +3,105 @@ tags:
 - vision
 - image-matching
 inference: false
----
+---
+
+
+# SuperPoint
+
+## Overview
+
+The SuperPoint model was proposed
+in [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel
+DeTone, Tomasz Malisiewicz and Andrew Rabinovich.
+
+This model is the result of a self-supervised training of a fully-convolutional network for interest point detection and
+description. The model is able to detect interest points that are repeatable under homographic transformations and
+provide a descriptor for each point. The use of the model in its own is limited, but it can be used as a feature
+extractor for other tasks such as homography estimation, image matching, etc.
+
+The abstract from the paper is the following:
+
+*This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a
+large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our
+fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and
+associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography
+approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g.,
+synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able
+to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other
+traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches
+when compared to LIFT, SIFT and ORB.*
+
+## How to use
+
+Here is a quick example of using the model to detect interest points in an image:
+
+```python
+from transformers import AutoImageProcessor, AutoModel
+import torch
+from PIL import Image
+import requests
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = AutoImageProcessor.from_pretrained("stevenbucaille/superpoint")
+model = AutoModel.from_pretrained("stevenbucaille/superpoint")
+
+inputs = processor(image, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+The outputs contain the list of keypoint coordinates with their respective score and description (a 256-long vector).
+
+You can also feed multiple images to the model. Due to the nature of SuperPoint, to output a dynamic number of keypoints,
+you will need to use the mask attribute to retrieve the respective information :
+
+```python
+from transformers import AutoImageProcessor, AutoModel
+import torch
+from PIL import Image
+import requests
+
+url_image_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image_1 = Image.open(requests.get(url_image_1, stream=True).raw)
+url_image_2 = "http://images.cocodataset.org/test-stuff2017/000000000568.jpg"
+image_2 = Image.open(requests.get(url_image_2, stream=True).raw)
+
+images = [image_1, image_2]
+
+processor = AutoImageProcessor.from_pretrained("stevenbucaille/superpoint")
+model = AutoModel.from_pretrained("stevenbucaille/superpoint")
+
+inputs = processor(images, return_tensors="pt")
+outputs = model(**inputs)
+
+for i in range(len(images)):
+    image_mask = outputs.mask[i]
+    image_indices = torch.nonzero(image_mask).squeeze()
+    image_keypoints = outputs.keypoints[i][image_indices]
+    image_scores = outputs.scores[i][image_indices]
+    image_descriptors = outputs.descriptors[i][image_indices]
+```
+
+You can then print the keypoints on the image to visualize the result :
+```python
+import cv2
+for keypoint, score in zip(image_keypoints, image_scores):
+    keypoint_x, keypoint_y = int(keypoint[0].item()), int(keypoint[1].item())
+    color = tuple([score.item() * 255] * 3)
+    image = cv2.circle(image, (keypoint_x, keypoint_y), 2, color)
+cv2.imwrite("output_image.png", image)
+```
+
+This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
+The original code can be found [here](https://github.com/magicleap/SuperPointPretrainedNetwork).
+
+```bibtex
+@inproceedings{detone2018superpoint,
+  title={Superpoint: Self-supervised interest point detection and description},
+  author={DeTone, Daniel and Malisiewicz, Tomasz and Rabinovich, Andrew},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
+  pages={224--236},
+  year={2018}
+}
+```