So101 Segmentation Model

--- library_name: ultralytics tags: - yolo - yolo11 - instance-segmentation - robotics - so101 pipeline_tag: image-segmentation model_index: - name: SO101 Nexus Segmentation results: - task: type: instance-segmentation --- # SO101 segmentation model This is a model for segementation of images of the [so101 robot arm](https://github.com/TheRobotStudio/SO-ARM100), it was fine tuned over yolo11s  ## Sample code Here's some sample code to use it ```python import cv2 import numpy as np from ultralytics import YOLO model = YOLO("weights/best.pt") cap = cv2.VideoCapture("test_video.mp4") w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) fourcc = cv2.VideoWriter_fourcc(*"avc1") out = cv2.VideoWriter("comparison_output.mp4", fourcc, fps, (w * 2, h)) print("Generating side-by-side video...") while cap.isOpened(): ret, frame = cap.read() if not ret: break results = model(frame) left_side = frame black_bg = np.zeros_like(frame) right_side = results[0].plot(img=black_bg, boxes=False, labels=True) combined_frame = np.hstack((left_side, right_side)) out.write(combined_frame) cap.release() out.release() print("Done! Check comparison_output.mp4") ``` Disclaimer : I vibe coded most of the code here, since it was one-time use code and I don't expect to publish it anywhere, I used https://github.com/johnsutor/so101-nexus to generate the synthetic images

Source context: 24 downloads · 5 likes · Pipeline image-segmentation · Library ultralytics · Repo riversnow/so101-segmentation-model