* Update README.md
Info for Ep 2 of Ultralytics Live Sessions
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* Update README.md
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* Update image link
Make sure we update the href field in the image so when users click the image they go directly to the YouTube live page.
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* Update README.md
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* Added basic flag to enable reusing last task clearml
* Added option to provide task ID to override
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* Use exist_ok argument instead
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* Update streams.txt default
Signed-off-by: Colin Wong <cwongmath@gmail.com>
* Change streams list extension to .streams
* Read txt as media per line
* Missed one
* Missed another one
* Update dataloaders.py
* Update detect.py
* Update dataloaders.py
* Update detect.py
* Update predict.py
* Update predict.py
* Update README.md
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* process_mask_native() cleanup
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* fix arg name
* cleanup anno_json
* Remove scale_image
* Remove scale_image
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* update to native
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* Connect opt to clearml args
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* Update clearml_utils.py
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* Add docker info for ClearML remote execution
* add additional clearml options to handle different python versions
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when there is `nan` in evolve.csv pandas read it as str
remove the space before fix that
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* fix plot&&speed up
* fix segment save-txt
* fix channel
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* Scope gitpyhon import in `check_git_info()`
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* Added a tutorial notebook for classification.
* Split a cell so that there is less room for error when a user pastes their own code snippet. Also added an active learning section at the end.
* Added a section to the classification tutorial notebook about the various methods of input for `classify/predict.py`.
* Updated link to colab
* WIP commit to show some of the errors when trying to match the main tutorial.
* Refactored the classification tutorial to be closer to the main tutorial.
* Update tutorial.ipynb
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* Update tutorial.ipynb
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* New global TQDM_BAR_FORMAT
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* Simplify dataloader tqdm descriptions
@AyushExel this should help our tqdm dataloader messages fit better within a single line in our Colab notebooks and also help avoid confusion about missing/empty labels, now combined into 'backgrounds'.
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* Update dataloaders.py
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* fix: try 2 - prevent logging config clobbering
Previous behavior: loading this repository with `torch.hub.load` clobbers the existing logging configuration by modifying the root logger's configuration.
New behavior: loading this repository with `torch.hub.load` only clobbers the logging configuration for logger `yolov5` and its descendants. This is done in a way compatible with Google Colab
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* chore: fill in comment
no-op so a pre-commit hook can auto-format files
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* Apply make_divisible for onnx models in Autoshape
At line 697 we have this `make_divisible` function for pytorch models.
* Context: we want to run inference on varied input sizes instead of fixed image size.
* When I test an image of size [720, 720] for a pytorch model (e.g., yolov5n.pt), we can see that it will be reshaped to [736, 736] by the function. This is as expected.
* When I test the same image for the onnx model (e.g., yolov5n.onnx, exported with `--dynamic`), I got an error and it's due to the indivisible problem
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running Concat node. Name:'Concat_143' Status Message: concat.cc:156 PrepareForCompute Non concat axis dimensions must match: Axis 3 has mismatched dimensions of 45 and 46
```
The simple solution is to enable the `make_divisible` function for onnx model too.
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* revise indent
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* Apply make_divisible to all formats
All formats from DetectMultiBackend should have default stride=32
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Talia Bender
pre-commit-ci[bot]
Leander van Eekelen
Victor Sonck
Colin Wong
Glenn Jocher
Michael Sutton
Michael Ben ami
Laughing
Hu Ye
Paul Guerrie
Ryan Echols
janus-zheng