Comments (4)
👋 Hello @VanillaMacchiato, 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.
from ultralytics.
@VanillaMacchiato hello,
Thank you for reporting this issue and for providing a detailed description along with your modifications. It's great to see your initiative in troubleshooting the problem! Let's address your concerns:
-
Reproducibility: Your steps to replicate the issue are clear and comprehensive. This will help us reproduce the bug on our end. Thank you for that!
-
Version Check: Please ensure you are using the latest versions of
torch
andultralytics
. You can upgrade them using:pip install --upgrade torch ultralytics
-
Code Modification: Regarding your modification to avoid using the
with
statement:ex = self.net.create_extractor() ex.input(self.net.input_names()[0], mat_in) y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())]
While this change seems to resolve the error, the
with
statement is generally used to ensure that resources are properly managed and released. Removing it might lead to resource leaks or other unintended side effects, especially in long-running applications. -
Next Steps:
- Testing: Continue monitoring the memory usage and performance over extended periods to ensure no resource leaks occur.
- PR Submission: If you are confident in your changes and have thoroughly tested them, feel free to submit a Pull Request (PR). We appreciate contributions from the community!
If you encounter any further issues or have additional questions, please let us know. We're here to help!
from ultralytics.
You can use this repo I created which provides a docker image with python3.11 to run any ultralytics yolo models on Jetson Nano with torch gpu or tensorrt
You can expect about 60-70ms average end-to-end with yolov8n and yolov10n on the Jetson with Python3.11
from ultralytics.
Hi @jasonlytehouse,
Thank you for sharing your Docker repo! It sounds like a valuable resource for the community. 🚀
Regarding the issue you reported, it's great that you've found a workaround. However, using the with
statement ensures proper resource management, which is crucial for long-running applications. Removing it might lead to resource leaks.
To further investigate and provide a robust solution, could you please confirm if the issue persists with the latest versions of torch
and ultralytics
? You can upgrade them using:
pip install --upgrade torch ultralytics
If the problem continues, please provide a minimal reproducible example following our guide. This will help us reproduce and address the issue more effectively.
Looking forward to your response!
from ultralytics.
Related Issues (20)
- add last_hidden_state function get last layer vector HOT 2
- Keeping model ready to detection HOT 2
- How to use text training in a classification model HOT 4
- I still get this : ModuleNotFoundError: No module named 'numpy._core' when I try all 3 ways mentioned above HOT 2
- How to get an output as a timestamp? HOT 6
- Large Output Video Size with Yolov8 Predict Mode HOT 2
- confusion matrix & index HOT 1
- How to reduce to 100 epochs without retraining after 200 epochs of simulation? HOT 3
- UnboundLocalError for 'ckpt_file' while running model.tune() HOT 2
- AttributeError: Can't get attribute 'v10DetectLoss' on <module 'ultralytics.utils.loss' from 'C:\\Users\\JFerreira\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\ultralytics\\utils\\loss.py' HOT 1
- Changing the C2f block fixes the pruning but how can I make it work with its own architecture? HOT 1
- Picking instance segmentation in roboflow for yolov8-obb HOT 5
- Yolo v10 is slower than v8? HOT 9
- Error message when export tensorrt in Jetpack 4 docker container. HOT 4
- yolov8 with multi cameras (using only CPU) HOT 5
- GPU memory usage issue
- how can I predict when my ch >4 HOT 1
- what's the meaning of (40 CPUs, 502.2 GB RAM, 15.6/18.3 GB disk)? HOT 1
- Can not export yolov10 model to paddlepaddle HOT 2
- Yolov8 loads other datasets HOT 1
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