* Single checkpoint resume
* Update train.py
* Add hyp
* Add hyp
* Add hyp
* FIX
* avoid resume on url data
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* avoid resume on url data
* avoid resume on url data
* Update
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* Add remote ultralytics and check git status with that
* Simplify
* Update general.py
* Update general.py
* s fix
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Checking if ``/workspace`` exists is not a reliable method to check if
the process runs in a docker container.
Reusing the logic from the npm "is-docker" package to check if the
process runs in a container.
References: https://github.com/sindresorhus/is-docker/blob/main/index.jsFixes#8710.
Co-authored-by: Maximilian Strobel <Maximilian.Strobel@infineon.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Fix BGR->RGB Bug in albumentations https://github.com/ultralytics/yolov5/issues/8641
* Change transform methode from cv2 to numpy
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* Simplify
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* New `HUBDatasetStats()` class
Usage examples:
```
from utils.dataloaders import *
stats = HUBDatasetStats('coco128.yaml', autodownload=True) # method 1
stats = HUBDatasetStats('path/to/coco128_with_yaml.zip') # method 1
stats.get_json(save=False)
stats.process_images()
```
@kalenmike
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* assert torch!=1.12.0 for DDP training
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* Refactor optimizer initialization
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* Add `--hard-fail` list argument to benchmarks for CI
Will cause CI to fail on a benchmark failure for given indices.
* Update ci-testing.yml
* Attempt Failure (CI should fail)
* Update benchmarks.py
* Update export.py
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* attempt at reproducibility
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* use deterministic algs
* fix everything :)
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* revert dataloader changes
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* process_batch as np
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* Add filesize to benchmarks.py
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* HUB dataset_stats() error reporting
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* Reject webcam inference on Colab/Kaggle
Improve user error understanding for https://github.com/ultralytics/yolov5/issues/8180
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* AutoBatch checks against failed solutions
@kalenmike this is a simple improvement to AutoBatch to verify that returned solutions have not already failed, i.e. return batch-size 8 when 8 already produced CUDA out of memory.
This is a halfway fix until I can implement a 'final solution' that will actively verify the solved-for batch size rather than passively assume it works.
* Update autobatch.py
* Update autobatch.py
