Comments (8)
π Hello @zp2546265641, thank you for your interest in Ultralytics YOLOv8 π! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
If this is a π Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training β Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
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Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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and now i haven't learned to train a new model, just due to my task, i do this operation
from ultralytics.
and now i haven't learned to train a new model, just due to my task, i do this operation
i run it on ubuntu20.04, and use it with pip install
from ultralytics.
Hello!
If you've updated the label names in your coco8.yaml
file but still see the old names during predictions, it's likely that the model you are using (yolov8n.pt
) was trained with the original label names and thus retains them internally.
For immediate changes in label names without retraining, you can manually adjust the names
attribute of your model after loading it, before running predictions. Here's a quick Python snippet to help you do that:
from ultralytics import YOLO
# Load your model
model = YOLO('/home/zp/test/yolov8n.pt')
# Update class names directly
model.names = {71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'Rotor', 75: 'vase', 76: 'scissors'}
# Now run prediction
results = model.predict(source='/home/zp/εΎη', save=True, show=True)
This way, you can ensure the predictions use the updated labels. Let me know if this helps or if you have any more questions!
from ultralytics.
Hello!
If you've updated the label names in your
coco8.yaml
file but still see the old names during predictions, it's likely that the model you are using (yolov8n.pt
) was trained with the original label names and thus retains them internally.For immediate changes in label names without retraining, you can manually adjust the
names
attribute of your model after loading it, before running predictions. Here's a quick Python snippet to help you do that:from ultralytics import YOLO # Load your model model = YOLO('/home/zp/test/yolov8n.pt') # Update class names directly model.names = {71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'Rotor', 75: 'vase', 76: 'scissors'} # Now run prediction results = model.predict(source='/home/zp/εΎη', save=True, show=True)This way, you can ensure the predictions use the updated labels. Let me know if this helps or if you have any more questions!
thank you very muchοΌi can realize my goal nowοΌ
from ultralytics.
Hello!
I'm glad to hear that the solution worked for you! If you have any more questions or need further assistance as you continue working with the YOLO models, feel free to reach out. Happy detecting! π
from ultralytics.
Hello!
I'm glad to hear that the solution worked for you! If you have any more questions or need further assistance as you continue working with the YOLO models, feel free to reach out. Happy detecting! π
thank you for your helpοΌi am glad to join such a big family! i have a new problem today and i just raise a new question in community! wishes for your reply!
from ultralytics.
Hello!
It's great to have you in the community! I'll take a look at your new question and respond there shortly. Keep the queries coming, and we're here to help! π
Happy detecting!
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Related Issues (20)
- yolov10-SCDown return error HOT 4
- Questions about using segmentation in TensorRT HOT 5
- video Inference is too slow in realtime HOT 2
- KeyError: 'Silence' while training YOLOv9 HOT 3
- Can .val use test data from custom yaml for evaluation? HOT 2
- Clarification on YOLOv8 fine-tuning HOT 12
- Get masks from model output0 and output1 HOT 2
- Confusion metrix HOT 2
- 'list' object has no attribute 'masks' HOT 6
- Dataset not found β οΈ, missing path HOT 6
- Trying to show the XY value for detecting objects on real-time
- Request for mAP of different scale HOT 8
- FATAL ERROR! reclaim_blob_allocator get wild allocator in Jetson Nano with NCNN Inference HOT 4
- data lable wrong when train yoloworld HOT 19
- Trained YOLOv8 model converted to CoreML doesn't give any predictions HOT 10
- About glean-t and yolov9-t HOT 6
- When I install torch_image, imgsz doesn't work. HOT 3
- Train subclass in Coco data set HOT 4
- Oriented Bounding box health check HOT 3
- [YoloV8] Torch compile model shows metrics degradation on the coco128 dataset HOT 4
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