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The code snippet was wrong, this PR fixes it.

README.md

@@ -3,7 +3,6 @@ datasets:
 - michaelyuanqwq/roboseg
 license: mit
 pipeline_tag: image-segmentation
-library_name: transformers
 tags:
 - segmentation
 - robotics
@@ -23,75 +22,10 @@ Visual augmentation has become a crucial technique for enhancing the visual robu
 
 ## Usage
 
-This model is a Robo-SAM checkpoint and can be loaded using the Hugging Face `transformers` library with `trust_remote_code=True`. It can be used for semantic robot segmentation.
+Refer to the [official GitHub repository](https://github.com/michaelyuancb/roboengine).
 
-```python
-from transformers import AutoProcessor, AutoModel
-from PIL import Image
-import torch
-import numpy as np
+## Citation
 
-# Load model and processor
-# Make sure you have installed `transformers` and `torch`
-# If you encounter errors, try `pip install torch` and `pip install transformers`
-model = AutoModel.from_pretrained("michaelyuanqwq/roboengine-sam", trust_remote_code=True)
-processor = AutoProcessor.from_pretrained("michaelyuanqwq/roboengine-sam", trust_remote_code=True)
-
-# Example image input: replace 'your_robot_image.png' with the actual path to your image.
-# You can find example images in the original GitHub repository:
-# https://github.com/michaelyuancb/roboengine/tree/main/assets
-try:
-    # Create a dummy image if file not found for demonstration
-    try:
-        raw_image = Image.open("your_robot_image.png").convert("RGB")
-    except FileNotFoundError:
-        print("Sample image 'your_robot_image.png' not found. Creating a dummy white image for demonstration.")
-        raw_image = Image.new('RGB', (512, 512), color = 'white')
-
-    # Prepare inputs for semantic robot segmentation
-    # The model expects input points or bounding boxes. A central point is often used
-    # as a default to prompt for the main object (robot) in the image.
-    input_points = [[[raw_image.height / 2, raw_image.width / 2]]]
-
-    inputs = processor(raw_image, input_points=input_points, return_tensors="pt")
-    # Move inputs to the appropriate device (e.g., GPU if available)
-    if torch.cuda.is_available():
-        for k,v in inputs.items():
-            if isinstance(v, torch.Tensor):
-                inputs[k] = v.to(model.device)
-
-    # Perform inference
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    # Post-process masks
-    # The output `outputs.pred_masks` contains the predicted masks.
-    # `post_process_masks` converts them to original image dimensions.
-    masks = processor.post_process_masks(
-        outputs.pred_masks.cpu(),
-        inputs["original_sizes"].cpu(),
-        inputs["reshaped_input_sizes"].cpu()
-    )[0] # Take the masks for the first image in the batch
-
-    # `masks` is a list of dictionaries, each describing a segmented object.
-    # The 'segmentation' key contains a boolean NumPy array.
-    if masks:
-        # Assuming the first mask is the primary robot segmentation
-        robot_mask_array = masks[0]['segmentation'].numpy()
-        # Save the mask as an image (e.g., black where not robot, white where robot)
-        Image.fromarray(robot_mask_array.astype(np.uint8) * 255).save("robot_segmented_mask.png")
-        print("Robot segmentation mask saved as robot_segmented_mask.png")
-    else:
-        print("No masks were generated for the input image.")
-
-except Exception as e:
-    print(f"An error occurred during usage example: {e}")
-    print("Please ensure all dependencies are installed and provide a valid image path.")
-
-```
-For a more comprehensive understanding and usage of RoboEngine as a full toolkit for robot data augmentation, please refer to the [official GitHub repository](https://github.com/michaelyuancb/roboengine).
-
-## BibTex
 ```bibtex
 @article{yuan2025roboengine,
   title={RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation},