-
-
Notifications
You must be signed in to change notification settings - Fork 16.1k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
YOLOv5 to Tensorflow Lite #11384
Comments
👋 Hello @Christalker123, 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. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf 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 |
@Christalker123 hi! Conversion to TensorFlow Lite from PyTorch is still experimental, so it is expected that the model may not perform the same after conversion. Additionally, it's recommended to use the official TensorFlow Lite converter to convert models to TFLite format. |
@glenn-jocher Hi thank you for the response sir. May i ask a question sir? During training sir of my mode and the .yaml file formation of the classes is like this: nc: 10 then when i convert my model.pt into model.tflite so the label txt file should be also like this: ? names: ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine'] or its ok if its like this : |
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. For additional resources and information, please see the links below:
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed! Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ |
@Christalker123 Hello! Your label txt file should indeed match the exact class names and order as specified in the .yaml file, such as Using anything other than this format in the label txt file could cause class indices and labels to not match, potentially resulting in issues during inference. Check that your label txt file is structured correctly, and ensure that the class indices and labels align in both the .yaml file and the label txt file. Good luck! |
Search before asking
Question
Hi! I trained YOLOv5 model. I validated it and tested it using detect.py. and the prediction really works perfectly. So I converted the model into tensorflow lite using this 'python export.py --weights yolov5s.pt --include tflite'. Then i integrated the tflite into android studio and so when i tested it. It didn't work correctly not same as the testing before it was converted. Is this really a problem? Theoretically maybe i should convert it to onnx first and then tensorflow lite. So the question is it gonna affect the accuracy of the model if we converted it to tflite?
Additional
No response
The text was updated successfully, but these errors were encountered: