iDetection models

[Vadbeg]

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Question

How did you convert the models for iDetection? Did you use coreml or torchscript? And if you used CoreML, please share the method with us. Because original script doesn't export Detection head. Screenshot from Netron.

Additional

This repo suggests to use torchscript model:
https://github.com/pytorch/ios-demo-app

[github-actions]

👋 Hello @Vadbeg, 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.

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Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

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• Docker Image. See Docker Quickstart Guide

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If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@Vadbeg your code may be out of date, all export formats contain all model modules including Detect.

[Vadbeg]

@glenn-jocher Okay, please, try the command below and send the screenshot of the resulting model to Neutron.

python export.py --include coreml --img 640

Because with the latest version of code, when I run script for detection:

python detect.py --weights ../weights/best.mlmodel --source ../data/images/test --img-size 640 --hide-labels --hide-conf

I get this error:

Traceback (most recent call last):
  File "detect.py", line 244, in <module>
    main(opt)
  File "detect.py", line 239, in main
    run(**vars(opt))
  File "/Users/vadim.tsitko/.virtualenv/yolov5-env/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
    return func(*args, **kwargs)
  File "detect.py", line 115, in run
    pred = model(im, augment=augment, visualize=visualize)
  File "/Users/vadim.tsitko/.virtualenv/yolov5-env/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/Users/vadim.tsitko/Projects/count-yolo/yolov5/models/common.py", line 362, in forward
    box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
KeyError: 'coordinates'

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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[congxing]

I am seeing the same issue using the latest version.

[glenn-jocher]

@Vadbeg @congxing good news 😃! Your original issue may now be fixed ✅ in PR #6195. This PR adds support for YOLOv5 CoreML inference.

!python export.py --weights yolov5s.pt --include coreml  # CoreML export
!python detect.py --weights yolov5s.mlmodel  # CoreML inference (MacOS-only)
!python val.py --weights yolov5s.mlmodel  # CoreML validation (MacOS-only)

model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.mlmodel')  # CoreML PyTorch Hub model

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

[congxing]

Thanks for fixing @glenn-jocher. I verified it was working on yolov5.mlmodel. However, for a customized model which is trained on another dataset, it seems the exported coreml model showing old labels using detect.py.

For example, I trained the model on a custom dataset with two labels ['pet', 'person']. Using best.pt, the detected box is marked as 'person', but best.mlmodel shows bicycle instead.

[glenn-jocher]

@congxing ah yes, thanks for the feedback! The CoreML models do not contain class names embedded, these are sourced from the data.yaml, which defaults to COCO128. I'll think about how to handle this better:

yolov5/detect.py

Line 220 in 00d7b97

parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')

[congxing]

Got it. Thanks for the pointer.