* 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
Signed-off-by: Ryan Echols <ryan@shadylakemedia.com>
* 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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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.
Signed-off-by: Ryan Echols <ryan@shadylakemedia.com>
Signed-off-by: Ryan Echols <ryan@shadylakemedia.com>
* Fix dataloader filepath modification to perform only once and not for all occurences of string
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* cleanup
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Remove box area function and support expandable bbox_iou() calls.
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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* Mapped project and name to ClearML
* Add project and task name docs
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Update common.py
We have a division at line 694, and then a multiplication at line 695, so it makes `y*g` not an integer. And since `shape1` will be used at line 697 to ensure the size is divisible by the `stride`, this may lead to different image size.
In my experiment, my image is [640, 640], it's divisible by the default stride 32, but I found that the result is changed to [672, 672] after line 697. So the final detection result is slightly different from that directly using the `detect.py` script, which does not call the AutoShape methods.
Signed-off-by: janus-zheng <106574221+janus-zheng@users.noreply.github.com>
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New 160-image MNIST subset composed of first 8 examples of each class. Suitable for fast CI.
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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* AutoCache
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* AutoCache
* AutoCache
* AutoCache
* AutoCache
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Glenn Jocher
Paul Guerrie
Ryan Echols
janus-zheng
tripleMu
Ryan Echols
Amol Dumrewal
Rubén Usamentiaga
Victor Sonck
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