Upload README.md with huggingface_hub

README.md

@@ -20,6 +20,7 @@ Disclaimer: The team releasing Mask2Former did not write a model card for this m
 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.
 
@@ -34,9 +35,9 @@ You can use this particular checkpoint for instance segmentation. See the [model
 Here is how to use this model:
 
 ```python
-import requests
 import torch
-from PIL import Image
+import torchvision
+from huggingface_hub import hf_hub_download
 from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
 
 
@@ -46,7 +47,7 @@ model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/video-mask
 
 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_frames = [image_processor(images=frame, return_tensors="pt").pixel_values for frame in video]
 video_input = torch.cat(video_frames)
 
 with torch.no_grad():