Comments (2)
π Hello @DaMuleX, 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.
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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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Hello,
Thank you for providing such a detailed description of the issue you're encountering. It sounds like the main problem revolves around the discrepancy between the validation results and the predictions when using the same images.
Given the details you've shared, it seems like the confidence threshold might be set too high during prediction, which could be why no objects are being detected. Since setting the confidence threshold to 0 yields predictions (albeit many), this suggests that the model does have some predictive capability but might be overly conservative at the default threshold.
Here are a few steps you can try:
-
Adjust the Confidence Threshold: Lower the confidence threshold slightly from the default value and observe if detections start appearing without overwhelming the output.
-
Check Preprocessing: Ensure that the image preprocessing during prediction matches that of the training/validation phase. Any discrepancy in preprocessing can lead to significant differences in model performance.
-
Review Model Output on GPU: Since you mentioned issues specifically when predicting on the GPU, it might be useful to ensure that the model and data are correctly transferred to the GPU. You can explicitly ensure this by checking
model.to('cuda')
andimages.to('cuda')
in your prediction script. -
Debugging Tip: Temporarily modify your prediction script to output intermediate values and confirm that all parts of the pipeline are functioning as expected. This might include checking the sizes and values of tensors after each significant step.
If these steps do not resolve the issue, it might be helpful to look deeper into the model's training logs and the exact differences in the setup between validation and prediction phases.
I hope these suggestions help! Please let us know how it goes or if you need further assistance.
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Related Issues (20)
- yolov8 for web-camera use to classification HOT 2
- Weights combining HOT 5
- I am getting error! Help Me to Fix it i am confused HOT 2
- export yolov8 format HOT 8
- RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by [rank0]: making sure all `forward` function outputs participate in calculating loss. HOT 2
- can not find the data correctly when use DDP train HOT 2
- Hybrid agnostic NMS HOT 6
- How do I correctly interpret and use the output from the OBB version of Yolov8 for 360ΒΊ prediction? HOT 8
- False Positive of YOLOv8 for Object Detection HOT 1
- TypeError: object of type 'int' has no len() HOT 1
- model not get optimized HOT 4
- gradio supports real-time detection of images captured in the camera. HOT 4
- Yolov8 trains 100 epochs on the coco8 dataset with a map of 0 HOT 4
- Problem with ONNX model HOT 12
- cls_loss and dfl_loss suddenly spike in the last 10 epochs HOT 3
- Training yolov9-seg Times Error HOT 2
- Precision Recall Curve HOT 4
- Split model size HOT 9
- When training in google colab environment, it won't show the precision as well as recall, mAP during training process. HOT 1
- Does not see several A2 video cards HOT 7
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