Update README.md

README.md

@@ -4,45 +4,18 @@ pipeline_tag: mask-generation
 library_name: sam2
 ---
 
-Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the [SAM 2 paper](https://arxiv.org/abs/2408.00714) for more information.
+MedSAM2 Small - CoreML Version
+MedSAM2 Small is a specialized version of SAM2 for medical image segmentation tasks, now available for use with CoreML. This model is optimized to work seamlessly on Apple devices, enabling efficient, on-device predictions. To get started, follow the instructions below.
 
-The official code is publicly release in this [repo](https://github.com/facebookresearch/segment-anything-2/).
+For detailed information, refer to the SAM2 paper and the official repository. The official code is publicly release in this [repo](https://github.com/facebookresearch/segment-anything-2/).
 
 ## Usage
 
-For image prediction:
+1. Download the .zip files containing the CoreML model from the repo.
+2. Extract the contents of the .zip file to a directory of your choice.
+3. Push to [SAM2 Studio](https://github.com/huggingface/sam2-studio)
+4. Open SAM2 Studio Repo on your Apple device using XCode.
 
-```python
-import torch
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-
-predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-small")
-
-with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-    predictor.set_image(<your_image>)
-    masks, _, _ = predictor.predict(<input_prompts>)
-```
-
-For video prediction:
-
-```python
-import torch
-from sam2.sam2_video_predictor import SAM2VideoPredictor
-
-predictor = SAM2VideoPredictor.from_pretrained("facebook/sam2-hiera-small")
-
-with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
-    state = predictor.init_state(<your_video>)
-
-    # add new prompts and instantly get the output on the same frame
-    frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):
-
-    # propagate the prompts to get masklets throughout the video
-    for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
-        ...
-```
-
-Refer to the [demo notebooks](https://github.com/facebookresearch/segment-anything-2/tree/main/notebooks) for details.
 
 ### Citation