metadata
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, it was fine tuned over yolo11s
Sample code
Here's some sample code to use it
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