Update README.md

README.md

@@ -37,25 +37,29 @@ Here is how to use this model:
 ```python
 import torch
 from PIL import Image
-import requests
-from transformers import SamModel, SamProcessor
+from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
-processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
 
-img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
-raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
-input_points = [[[450, 600]]]  # 2D location of a window in the image
+# load Mask2Former fine-tuned on COCO panoptic segmentation
+processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-coco-panoptic")
+model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-coco-panoptic")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt")
 
-inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
 with torch.no_grad():
     outputs = model(**inputs)
 
-masks = processor.image_processor.post_process_masks(
-    outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
-)
-scores = outputs.iou_scores
+# 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.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
+predicted_panoptic_map = result["segmentation"]
 ```
 
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).