Comments (6)
👋 Hello @mamdouhhz, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ 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 🐛 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.
Requirements
Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
Environments
YOLOv5 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 YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Introducing YOLOv8 🚀
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!
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 yolov5.
Hello there! 👋
Yes, you're onto something with memory limitations. The --imgsz
parameter in YOLOv5 specifies the image size to resize your images to during training. When you set a very large --imgsz
, such as 2880, it significantly increases GPU memory requirements. Your training likely doesn't start with --imgsz 2880
due to insufficient GPU memory to handle such large images.
A workaround is to use a smaller --imgsz
that fits within your GPU's memory limits. You can experiment starting with lower sizes and gradually increasing until you find the maximum size that works for your setup. Additionally, reducing the batch size can help accommodate larger image sizes, as it also reduces memory consumption.
Feel free to consult the docs for more insights on managing resource usage during training.
Let us know if you have any more questions. Happy training!
from yolov5.
thank you for your response, i have another question
the dimensions of my dataset's images are not the same they range from 1976 x 976 up to 3076 x 1536 and all images are labelled, if i resized the images so that all images are the same size and to be smaller in size then the labels will not work on the new image dimensions, how to solve this problem ? the labels are bbox coordinates and diesease class
from yolov5.
Hello again! 😊
No worries, YOLOv5 handles varying image sizes and label rescaling automatically. When you specify --imgsz
, YOLOv5 resizes your images to that size for training while appropriately scaling the bounding box coordinates in your labels, ensuring they still accurately represent the object locations in the resized images.
Just proceed with training by setting your desired --imgsz
parameter, and YOLOv5 will take care of the rest. No manual resizing or label adjustment needed on your part!
Remember, though, to choose an --imgsz
that balances between your GPU memory limitations and the need to maintain detail for accurate detection, especially for high-resolution datasets like yours.
Happy to help if you have more questions. Keep up the great work!
from yolov5.
Hello again! 😊
No worries, YOLOv5 handles varying image sizes and label rescaling automatically. When you specify
--imgsz
, YOLOv5 resizes your images to that size for training while appropriately scaling the bounding box coordinates in your labels, ensuring they still accurately represent the object locations in the resized images.Just proceed with training by setting your desired
--imgsz
parameter, and YOLOv5 will take care of the rest. No manual resizing or label adjustment needed on your part!Remember, though, to choose an
--imgsz
that balances between your GPU memory limitations and the need to maintain detail for accurate detection, especially for high-resolution datasets like yours.Happy to help if you have more questions. Keep up the great work!
Thank you very much
from yolov5.
You're welcome! 😄 If you have any more questions or need further assistance as you proceed, feel free to reach out. Happy training!
from yolov5.
Related Issues (20)
- Cannot select specific coda device HOT 2
- Run yolov5 using tensor rt HOT 1
- Is it possible to add ShuffleNetV2 as backbone in the official repo? HOT 2
- Memory Error When Training YOLOv5 Using Git Bash HOT 4
- How to use tensor rt in yolov5 detection HOT 1
- resume_evolve BUG!!! HOT 3
- Classification training model error HOT 2
- How do Yolo target assignments to anchors work? HOT 3
- roc curve HOT 5
- Confusion Matrix wrong output HOT 2
- Zero recall and zero precision even after 100 epochs and pretrained weights HOT 2
- May I ask yolov5 how to port the method of calculating P, R, AP, MAP in val.py to adapt to detect.py, what code need to be packed? HOT 2
- CONVERT yolov5 TO onxx and openvino format HOT 4
- Inference model after convert to tflite file. HOT 16
- No detections on custom data training HOT 2
- Freeze detection on training HOT 2
- Why is the number of FP, TP calculated by val.py for a certain class different from the number predicted by detect.py and then calculated? HOT 1
- Saving Augmented Images HOT 1
- TensorRT is slower than pytorch HOT 7
- The accuracy of the .pt model will decrease after being converted to .engine model. HOT 4
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