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* Update restapi.py
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* Add redundant weights download mirrors
This PR seeks to improve access worldwide to YOLOv5 weights. Universal access to AI for all is our core value, and we are against any censorship or restriction efforts.
I've uploaded the official YOLOv5 v6.1 weights to a primary backup bucket on GCP and to secondary backup on Google Drive at https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl. Autodownload with try the first two locations (GitHuB release assets and GCP bucket), and point users to the Google Drive folder if the first two fail.
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* support nomedia
* support nomedia for validation
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* Update train.py
* Revert no plot evolve
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* Revert plot_results
contains no media
* Update wandb_utils.py
* sync-bn cleanup
* Cleanup
* Rename nomedia -> noplots
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* Autoinstall TensorRT if missing
May resolve https://github.com/ultralytics/yolov5/issues/7464
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* Added a way to skip dependency auto-installation.
Setting the environment variable `YOLOv5_AUTOINSTALL=False` will
skip installing any missing dependencies as if the user had passed
`install=False` to `check_requirements`.
* Cleanup
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* add fp16 for coreml using --half
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* Update `git_describe()`
Add .git path check to avoid `fatal: not a git repository (or any of the parent directories): .git` printout
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* add version warning
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* Add support for different normalization layers.
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* set common default stride as 32
* restore default stride, and set it on argument optional
* fix wrong use of opt
* fix missing parameter of stride
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* fix format of parameters
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* Print dataset scan only `if RANK in (-1, 0)`
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* Copy wandb param dict before training to avoid overwrites.
Copy the hyperparameter dict retrieved from wandb configuration before passing it to `train()`. Training overwrites parameters in the dictionary (eg scaling obj/box/cls gains), which causes the values reported in wandb to not match the input values. This is confusing as it makes it hard to reproduce a run, and also throws off wandb's Bayesian sweep algorithm.
* Cleanup
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