Comments (4)
๐ Hello @jackfaubshner, thank you for your interest in YOLOv3 ๐! Please visit our โญ๏ธ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
If this is a ๐ Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training โ 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.
Requirements
Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
git clone https://github.com/ultralytics/yolov3 # clone
cd yolov3
pip install -r requirements.txt # install
Environments
YOLOv3 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 YOLOv3 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Introducing YOLOv8 ๐
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 ๐!
Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our YOLOv8 Docs for details and get started with:
pip install ultralytics
from yolov3.
@jackfaubshner hello!
Thank you for the detailed issue description. It seems like you're encountering two main problems when training YOLOv3-tiny: segmentation faults on powerful equipment and the "Killed" message on your CPU-only setup.
-
Segmentation Faults: This issue frequently relates to environment-specific constraints rather than the model itself. Ensure your PyTorch and CUDA versions are compatible. Also, try reducing the batch size to see if it alleviates the problem.
-
"Killed" Message: This typically happens due to an out-of-memory error, especially on systems with limited resources like your CPU-only laptop. The training process requires a considerable amount of RAM, and when you increase your batch size or your system runs out of memory, the OS might terminate the process. Try reducing the
--batch-size
(e.g., to 16 or 32) and see if it solves the issue.
Lastly, it's essential to keep your repository up to date, as mentioned in your logs. Though the message points towards cloning YOLOv5, it's just about ensuring your YOLOv3 version is current. For detailed investigations and advanced troubleshooting, consult the documentation at https://docs.ultralytics.com.
Keep in mind, the YOLO community and we at Ultralytics are here to help, and we appreciate your contribution to making YOLOv3 better! ๐
from yolov3.
@jackfaubshner hello!
Thank you for the detailed issue description. It seems like you're encountering two main problems when training YOLOv3-tiny: segmentation faults on powerful equipment and the "Killed" message on your CPU-only setup.
1. **Segmentation Faults**: This issue frequently relates to environment-specific constraints rather than the model itself. Ensure your PyTorch and CUDA versions are compatible. Also, try reducing the batch size to see if it alleviates the problem. 2. **"Killed" Message**: This typically happens due to an out-of-memory error, especially on systems with limited resources like your CPU-only laptop. The training process requires a considerable amount of RAM, and when you increase your batch size or your system runs out of memory, the OS might terminate the process. Try reducing the `--batch-size` (e.g., to 16 or 32) and see if it solves the issue.
Lastly, it's essential to keep your repository up to date, as mentioned in your logs. Though the message points towards cloning YOLOv5, it's just about ensuring your YOLOv3 version is current. For detailed investigations and advanced troubleshooting, consult the documentation at https://docs.ultralytics.com.
Keep in mind, the YOLO community and we at Ultralytics are here to help, and we appreciate your contribution to making YOLOv3 better! ๐
Thank you kind sir, I was able to get it to work on my old laptop, not that I am going to train on it, just wanna check if the code works. It would probably take more time to train on that laptop than the heat death of the universe
Also, yes, the issue with the workstation is probably with CUDA. It has CUDA 12.0 which no version of PyTorch supports
I'm gonna close this issue but is there any parameter I can add to the command below to train it on CPU only? Cause I don't think I can change the CUDA version on this workstation as other people are using it.
python3 train.py --data coco.yaml --epochs 300 --weight '' --cfg yolov3-tiny.yaml --batch-size 128
from yolov3.
@jackfaubshner, great to hear you got it working on your laptop, even if just for a test! Regarding training on the CPU, you can indeed run your training on a CPU by specifying the device. Just add --device cpu
to your command like so:
python3 train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 128 --device cpu
This tells the script to ignore any GPUs and run the training process on the CPU only. Keep in mind, as you've probably guessed, training on a CPU is significantly slower than on GPUs. ๐
Should you have any more questions or run into issues, feel free to ask. Happy training! ๐
from yolov3.
Related Issues (20)
- About the instructions and code comments HOT 3
- A hopelessly long try to replicate the YOLOv3 kernel HOT 2
- Change in the anchor boxes HOT 10
- โ๏ธClosed per Code of Conduct HOT 1
- no anchor_grid in V9.6.0 yolov3.pt HOT 5
- Convert YOLOv3 dataset format to YOLOv8 HOT 3
- What's the difference between it and Yolov3 by Joseph Redmon ? HOT 7
- Integrating YOLOv8 into YOLOv3 Ultralytics HOT 2
- Seeking Advice on Equivalent YOLOv5 Variant to Standard YOLOv3 HOT 1
- Unexpectedly large trained model size (~200 MB .pt and ~400 MB .onnx) HOT 4
- Training requires much more VRAM than v5/v8 and results in ~200 MB models comparing to <15 MB models of v5/v8 HOT 5
- how to train your yolov8?
- Need info regarding yolov3-tiny anchors, dataset creation and loss function. HOT 5
- Cannot compute loss function from best model HOT 1
- yolov3_ros input topic channel problem HOT 5
- yolov3.pt HOT 4
- ๅ ณไบ่ฐ็จๆจ็ไปฃ็ ๅ้ๅฐ็ไธไธไบ้ฎ้ข HOT 8
- Bug of incomplete information display HOT 2
- No module named 'ultralytics.yolo' HOT 2
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