datasets/detect/coco8-grayscale/ #20844
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Finally |
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👋 Hello, thank you for highlighting the COCO8-Grayscale dataset from Ultralytics 🚀! If you’re exploring or encountering any issues with this dataset, we recommend checking out the Docs which include many helpful Python and CLI usage examples. Many common questions may already be addressed there. If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us investigate further. If this is a custom training ❓ Question, please share as much detail as possible, including dataset image samples, training logs, and confirm you are following our Tips for Best Training Results. For community support, you can:
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StatusIf this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLO Modes and Tasks across macOS, Windows, and Ubuntu every 24 hours and on every commit. This is an automated response 🤖. An Ultralytics engineer will also review and assist you soon! |
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Hello, I did not notice this page before, so I trained my model on default rgb 3 channels and then predicted. Actually, all my native dataset images are gray images, I used a numpy operation to manually transfer it before training. I wonder that if I change channels to 1, will I be able to gain any improvements on my model. And do I need to modify both data.yaml and my customized yolo11n.yaml, right? |
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Great question—set channels: 1 in your dataset YAML and train; you don’t need to modify your custom yolo11n.yaml as the model’s first layer adapts automatically; if you previously replicated grayscale to 3 channels, accuracy differences are usually small while 1-channel can save memory, so a quick A/B run is the best way to confirm; for more on multi-channel handling, see the COCO8-Multispectral dataset page. |
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Dear Ultralytics, Hello. I am a learner and a user of Ultralytics-YOLO Software. At the moment, I am using Ultralytics for Classification/Segmentation/Tracking of my Livestream video. I have used the software well so far to classify/segment/track the 4 connectors in Livestream color video Now, I want to test the performance for a real scenario, where the Livestream camera will aceept only B/W images (The equipment has not been installed yet and I am trying to emulate future situation). Thank you for your assistance! |
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There’s no built-in switch in import cv2
from ultralytics import YOLO
model = YOLO("best.pt") # your det/seg model
cap = cv2.VideoCapture(0)
while True:
ok, frame = cap.read()
if not ok:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame_bw3 = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR) # BW content, kept 3-channel
results = model.track(source=frame_bw3, persist=True, verbose=False)
annotated = results[0].plot() # BGR
annotated_gray = cv2.cvtColor(annotated, cv2.COLOR_BGR2GRAY)
cv2.imshow("0", annotated_gray)
if cv2.waitKey(1) == 27: # ESC to quit
break
cap.release()
cv2.destroyAllWindows() |
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Thank you for such a prompt and precise answer! |
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Glad it helped—if you end up training a |
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datasets/detect/coco8-grayscale/
Explore the Ultralytics COCO8-Grayscale dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
https://docs.ultralytics.com/datasets/detect/coco8-grayscale/
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