Comments (2)
👋 Hello @Diogo-Valente2111, thank you for your interest in Ultralytics YOLOv8 🚀! 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 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training ❓ 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.
Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
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! The deterministic
parameter in YOLOv8 ensures that when enabled, the model will consistently produce the same output for the same input, which is crucial for reproducibility in experiments. Non-deterministic behaviors typically arise from certain GPU operations and multi-threading data loading techniques that can yield minor variations in results during different runs, even with the same inputs. In deep learning, common sources of non-determinism include certain convolutional operations and specific types of data augmentation. Opting for deterministic behavior might inhibit the model's ability to leverage these faster, non-deterministic operations, potentially leading to slower processing times. Hope this clarifies your doubt! 😊
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Related Issues (20)
- How do I run tracking with ultralytics on multiple GPUs for quicker processing? HOT 5
- How can i use MPII dataset for YoloV8-pose training HOT 2
- How to chose what metrics use as goal for model.tune? HOT 4
- Have to solve convert best.pt model to tflite HOT 3
- Have to convert best.pt model to tflite format HOT 2
- Detection and segmentation from same img source
- Run Yolov8 on Jetson Nano with TensorRt HOT 6
- Why doesn't YOLO-worldv2 use ImagePoolingAttn? HOT 3
- How to train model without color? HOT 3
- how to write a self-defined .yaml file for HICO-DET dataset HOT 3
- OSError: [WinError 126] The specified module could not be found. Error loading "C:\Users\User_Name\AppData\Roaming\Python\Python311\site-packages\torch\lib\shm.dll" or one of its dependencies. HOT 2
- Attempt to Replicate Validation Produces Worse Results HOT 2
- Yolov8 performance on Raspberry Pi 4B (8Gb) HOT 1
- Get results as YOLO annotation HOT 2
- Bug in torch.unique(return_counts=True) on MPS device results in incorrect counts and negative tensor dimensions HOT 2
- Model quantization with ONNX fails HOT 2
- How to train on multiple/single detection head for Yolov8 like Yolov9? HOT 2
- Is the angle value given by OBB correct? HOT 3
- YoloV8 CLI Training not generating Tensorflow events files HOT 2
- Fix import
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