Update README.md

README.md

@@ -1,67 +1,65 @@
----
-license: other
-tags:
-- vision
-- image-segmentation
-datasets:
-- coco
-widget:
-- src: http://images.cocodataset.org/val2017/000000039769.jpg
-  example_title: Cats
-- src: http://images.cocodataset.org/val2017/000000039770.jpg
-  example_title: Castle
----
-
-# Mask2Former
-
-Mask2Former model trained on Cityscapes semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
-](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). 
-
-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.
-
-![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png)
-
-## Intended uses & limitations
-
-You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other
-fine-tuned versions on a task that interests 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 Cityscapes semantic segmentation
-processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-cityscapes-semantic")
-model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-cityscapes-semantic")
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-inputs = processor(images=image, return_tensors="pt")
-
-with torch.no_grad():
-    outputs = model(**inputs)
-
-# 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
-predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
-# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
-```
-
-For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).
+---
+license: other
+tags:
+- vision
+- image-segmentation
+datasets:
+- coco
+widget:
+- src: http://images.cocodataset.org/val2017/000000039769.jpg
+  example_title: Cats
+- src: http://images.cocodataset.org/val2017/000000039770.jpg
+  example_title: Castle
+---
+
+
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+from PIL import Image
+import matplotlib.pyplot as plt
+
+# Load the processor and model
+model_name = "saninmohammedn/mask2former-deployment"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = Mask2FormerForUniversalSegmentation.from_pretrained(model_name)
+
+# Load an input image
+image_path = "your_image.jpg"  # Replace with your image path
+image = Image.open(image_path).convert("RGB")
+
+# Prepare the image for the model
+inputs = processor(images=image, return_tensors="pt")
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Post-process the predicted segmentation map
+predicted_map = processor.post_process_semantic_segmentation(
+    outputs, target_sizes=[image.size[::-1]]
+)[0].cpu().numpy()
+
+# Visualize the input and predicted segmentation map
+plt.figure(figsize=(10, 5))
+
+# Display original image
+plt.subplot(1, 2, 1)
+plt.imshow(image)
+plt.title("Original Image")
+plt.axis("off")
+
+# Display predicted segmentation map
+plt.subplot(1, 2, 2)
+plt.imshow(predicted_map, cmap="jet")
+plt.title("Predicted Segmentation Map")
+plt.axis("off")
+
+plt.tight_layout()
+plt.show()
+
+```