training fails converting Objects365 validation labels

[chang-1]

Search before asking

• I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

I want to train YoloV5 with the Objects365 dataset. The training set download and conversion worked flawlessly. However, I am getting this error with the zhiyuan_objv2_val.json file.

File "/oscar/data/tserre/jchang88/test/yolov5/train.py", line 848, in <module> main(opt) File "/oscar/data/tserre/jchang88/test/yolov5/train.py", line 623, in main train(opt.hyp, opt, device, callbacks) File "/oscar/data/tserre/jchang88/test/yolov5/train.py", line 176, in train data_dict = data_dict or check_dataset(data) # check if None File "/oscar/data/tserre/jchang88/test/yolov5/utils/general.py", line 575, in check_dataset r = exec(s, {"yaml": data}) # return None File "<string>", line 35, in <module> File "/oscar/data/tserre/jchang88/test/yolov5/env/lib/python3.10/site-packages/pycocotools/coco.py", line 82, in __init__ dataset = json.load(f) File "/oscar/runtime/software/external/miniconda3/23.11.0/lib/python3.10/json/__init__.py", line 293, in load return loads(fp.read(), File "/oscar/runtime/software/external/miniconda3/23.11.0/lib/python3.10/json/__init__.py", line 346, in loads return _default_decoder.decode(s) File "/oscar/runtime/software/external/miniconda3/23.11.0/lib/python3.10/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/oscar/runtime/software/external/miniconda3/23.11.0/lib/python3.10/json/decoder.py", line 353, in raw_decode obj, end = self.scan_once(s, idx) json.decoder.JSONDecodeError: Unterminated string starting at: line 1 column 182910524 (char 182910523)

What would be the solution? Thanks!

Additional

No response

[github-actions]

👋 Hello @chang-1, 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

[glenn-jocher]

@chang-1 hello! It looks like you're encountering a JSON decoding error with the Objects365 validation labels. This error typically indicates an issue with the JSON file itself, such as it being incomplete or corrupted. Here are a few steps you can take to resolve this issue:

1. Verify the JSON File: Ensure that the zhiyuan_objv2_val.json file has been fully downloaded without any interruptions. Sometimes, network issues can result in incomplete downloads.

2. Check for Corruption: Open the JSON file in a text editor and check for any obvious signs of corruption, especially near the end of the file. The error message points to an unterminated string, which could mean the file was cut off.

3. Re-download the File: If possible, try re-downloading the validation labels file from the original source to ensure you have a complete and uncorrupted version.

4. JSON Validation: Use a JSON validator tool to check for any syntax errors in the file. This can help identify the exact location of the issue within the JSON structure.

If after these steps you're still facing issues, it might be helpful to look into any specific requirements or known issues with the Objects365 dataset and YOLOv5 compatibility. For further assistance, you can refer to our documentation at https://docs.ultralytics.com/yolov5/ which might provide additional insights or steps for troubleshooting dataset issues.

Let us know how it goes, and if you have any more questions, feel free to ask!

[github-actions]

👋 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:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

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 ⭐