Update README.md

README.md

@@ -27,6 +27,101 @@ Our method leverages the pre-trained SAM model with only marginal parameter incr
 
 <img width="1096" alt="image" src="figures/architecture.jpg">
 
+**Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the original [SAM model card](https://github.com/facebookresearch/segment-anything).
+
+# Model Details
+
+The RobustSAM model is made up of 3 modules:
+  - The `VisionEncoder`: a VIT based image encoder. It computes the image embeddings using attention on patches of the image. Relative Positional Embedding is used.
+  - The `PromptEncoder`: generates embeddings for points and bounding boxes
+  - The `MaskDecoder`: a two-ways transformer which performs cross attention between the image embedding and the point embeddings (->) and between the point embeddings and the image embeddings. The outputs are fed
+  - The `Neck`: predicts the output masks based on the contextualized masks produced by the `MaskDecoder`.
+# Usage
+
+
+## Prompted-Mask-Generation
+
+```python
+from PIL import Image
+import requests
+from transformers import AutoProcessor, AutoModelForMaskGeneration
+
+# load the RobustSAM model and processor
+processor = AutoProcessor.from_pretrained("jadechoghari/robustsam-vit-base")
+model = AutoModelForMaskGeneration.from_pretrained("jadechoghari/robustsam-vit-base")
+
+# load an image from a url
+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")
+
+# we define input points (2D localization of an object in the image)
+input_points = [[[450, 600]]]  # example point
+
+```
+
+
+```python
+# process the image and input points
+inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda")
+
+# generate masks using the model
+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
+
+```
+Among other arguments to generate masks, you can pass 2D locations on the approximate position of your object of interest, a bounding box wrapping the object of interest (the format should be x, y coordinate of the top right and bottom left point of the bounding box), a segmentation mask. At this time of writing, passing a text as input is not supported by the official model according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844).
+For more details, refer to this notebook, which shows a walk throught of how to use the model, with a visual example! 
+
+## Automatic-Mask-Generation
+
+The model can be used for generating segmentation masks in a "zero-shot" fashion, given an input image. The model is automatically prompt with a grid of `1024` points
+which are all fed to the model. 
+
+The pipeline is made for automatic mask generation. The following snippet demonstrates how easy you can run it (on any device! Simply feed the appropriate `points_per_batch` argument)
+```python
+from transformers import pipeline
+
+# initialize the pipeline for mask generation
+generator = pipeline("mask-generation", model="jadechoghari/robustsam-vit-base", device=0, points_per_batch=256)
+
+image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
+outputs = generator(image_url, points_per_batch=256)
+```
+Now to display the generated mask on the image: 
+```python
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+
+# simple function to display the mask
+def show_mask(mask, ax, random_color=False):
+    if random_color:
+        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+    else:
+        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
+    
+    # get the height and width from the mask
+    h, w = mask.shape[-2:]
+    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    ax.imshow(mask_image)
+
+# display the original image
+plt.imshow(np.array(raw_image))
+ax = plt.gca()
+
+# loop through the masks and display each one
+for mask in outputs["masks"]:
+    show_mask(mask, ax=ax, random_color=True)
+
+plt.axis("off")
+
+# show the image with the masks
+plt.show()
+```
+
+
 
 ## Comparison of computational requirements
 <img width="720" alt="image" src='figures/Computational requirements.PNG'>