* Refactor for simplification
* cleanup
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* AutoBatch protect from negative batch sizes
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* Replace torchvision annotator with native local Annotator
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* Reduced conf_threshold to 0.25 to match defaults
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* fix: added transparent image and empty alt to social bar
* fix: remove underlines
* fix: hide underlines for all social links
* fix: always unzip into folder
* Single checkpoint resume
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* s fix
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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.
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* Fix BGR->RGB Bug in albumentations https://github.com/ultralytics/yolov5/issues/8641
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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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* 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
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
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* Cleanup
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* Fixes
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* Improve mAP0.5-0.95
Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function
Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.
Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s]
all 5000 36335 0.743 0.626 0.682 0.506
from pycocotools
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685
These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.
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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
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* Update train.py
* Revert no plot evolve
evolve plots do not contain any images
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contains no media
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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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* 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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* fix: disable usage of root logger
`logging.basicConfig` configures Python's root logger. This prohibits
fine control of logging, overwrites logging configuration done outside
the package, and is not best practice. Instead, the used logger is now
configured directly, and the root logger is untouched.
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* precommit: yapf
* align isort
* fix
# Conflicts:
# utils/plots.py
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* 🎉🆕 now can read Chinese image path.
use "cv2.imdecode(np.fromfile(f, np.uint8), cv2.IMREAD_COLOR)" instead of "cv2.imread(f)" for Chinese image path.
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Stems not working correctly for YOLOv5l with current .rstrip() implementation. After fix:
```
YOLOv5l summary: 468 layers, 46563709 parameters, 46563709 gradients, 109.3 GFLOPs
```
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* fix: add default font as fallback
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Glenn Jocher
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