Update README.md

README.md

@@ -14,24 +14,40 @@ widget:
   example_title: Palace
 ---
 
-# DPT (large-sized model) fine-tuned on ADE20k
+
+# Dense Prediction Transformer DPT (large-sized model) fine-tuned on ADE20k
 
 Dense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/isl-org/DPT). 
 
-Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face team.
+It is suited for image segmentation, specifically, semantic segmentation - tasks such as is seen the output below:
+
+| Input Image | Output Depth Image | 
+| --- | --- | 
+| ![input image](https://cdn-uploads.huggingface.co/production/uploads/641bd18baebaa27e0753f2c9/W-l_okUcVQRYwR0VAkk_q.png)  | ![Segmentation image](https://cdn-uploads.huggingface.co/production/uploads/641bd18baebaa27e0753f2c9/ETbeEDqVZE4Ut0QTjrfgc.png) |  
 
-## Model description
 
-DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for semantic segmentation.
+Disclaimer: The team releasing DPT did not write a model card for this model so this model card has been written by the Hugging Face in conjunction with Intel team.
 
-![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg)
+
+## Model description
+
+DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for semantic segmentation. It is based on MiDaS v3.0 as described in the paper above.
 
 ## Intended uses & limitations
 
 You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=dpt) to look for
 fine-tuned versions on a task that interests you.
 
-### How to use
+## Results:
+According to the authors, at the time of publication,  when applied to semantic segmentation, 
+dense vision transformers set a new state of the art on
+**ADE20K with 49.02% mIoU**. 
+
+We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at
+
+[DPT GitHub Repository](https://github.com/intel-isl/DPT)
+
+### How to use - Demonstration of Image Semantic Segmentation using DPTs
 
 Here is how to use this model:
 
@@ -40,16 +56,47 @@ from transformers import DPTFeatureExtractor, DPTForSemanticSegmentation
 from PIL import Image
 import requests
 
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+url = "http://images.cocodataset.org/val2017/000000026204.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-large-ade")
+feature_extractor = DPTImageProcessor .from_pretrained("Intel/dpt-large-ade")
 model = DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade")
 
 inputs = feature_extractor(images=image, return_tensors="pt")
 
 outputs = model(**inputs)
 logits = outputs.logits
+print(logits.shape)
+logits
+prediction = torch.nn.functional.interpolate(
+    logits,
+    size=image.size[::-1],  # Reverse the size of the original image (width, height)
+    mode="bicubic",
+    align_corners=False
+)
+
+# Convert logits to class predictions
+prediction = torch.argmax(prediction, dim=1) + 1
+
+# Squeeze the prediction tensor to remove dimensions
+prediction = prediction.squeeze()
+
+# Move the prediction tensor to the CPU and convert it to a numpy array
+prediction = prediction.cpu().numpy()
+
+# Convert the prediction array to an image
+predicted_seg = Image.fromarray(prediction.squeeze().astype('uint8'))
+
+# Define the ADE20K palette
+adepallete = [0,0,0,120,120,120,180,120,120,6,230,230,80,50,50,4,200,3,120,120,80,140,140,140,204,5,255,230,230,230,4,250,7,224,5,255,235,255,7,150,5,61,120,120,70,8,255,51,255,6,82,143,255,140,204,255,4,255,51,7,204,70,3,0,102,200,61,230,250,255,6,51,11,102,255,255,7,71,255,9,224,9,7,230,220,220,220,255,9,92,112,9,255,8,255,214,7,255,224,255,184,6,10,255,71,255,41,10,7,255,255,224,255,8,102,8,255,255,61,6,255,194,7,255,122,8,0,255,20,255,8,41,255,5,153,6,51,255,235,12,255,160,150,20,0,163,255,140,140,140,250,10,15,20,255,0,31,255,0,255,31,0,255,224,0,153,255,0,0,0,255,255,71,0,0,235,255,0,173,255,31,0,255,11,200,200,255,82,0,0,255,245,0,61,255,0,255,112,0,255,133,255,0,0,255,163,0,255,102,0,194,255,0,0,143,255,51,255,0,0,82,255,0,255,41,0,255,173,10,0,255,173,255,0,0,255,153,255,92,0,255,0,255,255,0,245,255,0,102,255,173,0,255,0,20,255,184,184,0,31,255,0,255,61,0,71,255,255,0,204,0,255,194,0,255,82,0,10,255,0,112,255,51,0,255,0,194,255,0,122,255,0,255,163,255,153,0,0,255,10,255,112,0,143,255,0,82,0,255,163,255,0,255,235,0,8,184,170,133,0,255,0,255,92,184,0,255,255,0,31,0,184,255,0,214,255,255,0,112,92,255,0,0,224,255,112,224,255,70,184,160,163,0,255,153,0,255,71,255,0,255,0,163,255,204,0,255,0,143,0,255,235,133,255,0,255,0,235,245,0,255,255,0,122,255,245,0,10,190,212,214,255,0,0,204,255,20,0,255,255,255,0,0,153,255,0,41,255,0,255,204,41,0,255,41,255,0,173,0,255,0,245,255,71,0,255,122,0,255,0,255,184,0,92,255,184,255,0,0,133,255,255,214,0,25,194,194,102,255,0,92,0,255]
+
+# Apply the color map to the predicted segmentation image
+predicted_seg.putpalette(adepallete)
+
+# Blend the original image and the predicted segmentation image
+out = Image.blend(image, predicted_seg.convert("RGB"), alpha=0.5)
+
+out
 ```
 
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt).