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README.md

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+---
+license: mit
+tags:
+- vision
+- image-segmentation
+datasets:
+- YouTubeVIS-2019
+---
+
+# Video Mask2Former
+
+Video Mask2Former model trained on YouTubeVIS-2019 instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Mask2Former for Video Instance Segmentation
+](https://arxiv.org/abs/2112.10764) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). 
+Video Mask2Former is an extension of the original Mask2Former paper released under the name, [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527).
+
+Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, 
+[MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without
+without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
+In the paper [Mask2Former for Video Instance Segmentation
+](https://arxiv.org/abs/2112.10764), the authors have shown that Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png)
+
+## Intended uses & limitations
+
+You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=video-mask2former) to look for other fine-tuned versions of this model that may interest you.
+
+### How to use
+
+Here is how to use this model:
+
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+
+
+# load Mask2Former fine-tuned on COCO instance segmentation
+processor = AutoImageProcessor.from_pretrained("facebook/video-mask2former-swin-tiny-youtubevis-2019-instance")
+model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask2former-swin-tiny-youtubevis-2019-instance")
+
+file_path = hf_hub_download(repo_id="shivi/video-demo", filename="cars.mp4", repo_type="dataset")
+video = torchvision.io.read_video(file_path)[0]
+video_frames = [image_processor(images=frame, return_tensors="pt", do_resize=True, size=(480, 640)).pixel_values for frame in video]
+video_input = torch.cat(video_frames)
+
+with torch.no_grad():
+    outputs = model(**video_input)
+
+# model predicts class_queries_logits of shape `(batch_size, num_queries)`
+# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+class_queries_logits = outputs.class_queries_logits
+masks_queries_logits = outputs.masks_queries_logits
+
+# you can pass them to processor for postprocessing
+result = processor.image_processor.post_process_video_instance_segmentation(outputs, target_sizes=[tuple(video.shape[1:3])])[0]
+# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
+predicted_video_instance_map = result["segmentation"]
+```
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).