* ClearML's set_report_period's time is defined in minutes not seconds.
https://clear.ml/docs/latest/docs/references/sdk/hpo_optimization_hyperparameteroptimizer/#set_report_period
set_report_period function takes in time in terms of minutes, not seconds.
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* Fix confidence threshold
The confidence is converted to a percentage on line 144, but it is being compared to a default conf_threshold value of a decimal value instead of percent value.
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* Revert "Fix confidence threshold"
This reverts commit f84a09967f.
* Fix confidence comparison
Fix the confidence percentage is being compared to a decimal value.
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* New TryExcept decorator
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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
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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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* Robust for DDP
* 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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* 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
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* 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.
* Update datasets.py
* Update __init__.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Stems not working correctly for YOLOv5l with current .rstrip() implementation. After fix:
```
YOLOv5l summary: 468 layers, 46563709 parameters, 46563709 gradients, 109.3 GFLOPs
```
* Update loss.py
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* Update loss.py
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* fix: add default font as fallback
Add default font as fallback if the downloading of the Arial.ttf font
fails for some reason, e.g. no access to public internet.
* Update plots.py
Co-authored-by: Maximilian Strobel <Maximilian.Strobel@infineon.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update AutoAnchor
* Update AutoAnchor
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* Update plots for Chinese fonts
* make is_chinese() non-str safe
* Add global FONT
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* Update general.py
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* New flag 'stop_training' in util.callbacks.Callbacks class to prematurely stop training from callback handler
* Removed most of the new checks, leaving only the one after calling 'on_train_batch_end'
* Cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* log best result in summary
* comment added
* add space for `flake8`
* log `best/epoch`
* fix `dimension` for epoch
ValueError: all the input arrays must have same number of dimensions
* log `best/` in `utils.logger.__init__`
* fix pre-commit
1. missing whitespace around operator
2. over-indented
Consider that the default value is CIOU，adjust the order of judgment could reduce the number of judgments.
And “elif CIoU:” didn't need 'if'.
Co-authored-by: 李杰 <360751194@qq.comqq.com>
* Check TensorRT>=8.0.0 version
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* Making `ar_thr` available as a hyperparameter
* Disabling ar_thr as hyperparameter and computing from the dataset instead
* Fixing bug in ar_thr computation
* Fix `ar_thr` to 100
* Find out a bug. When set batch_size = -1 to use the autobatch.
reproduce:
* Fix type conflict
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
When OpenCV retrieving image fail, original code would modify source images **inplace**, which may result in plotting bounding boxes on a black image. That is, before inference, source image `im0s[i]` is OK, but after inference before `Process predictions`, `im0s[i]` may have been changed.
* Remove NCOLS from tqdm
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* update
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* reformat
* Single-line argparser argument
* Update README.md
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* Update README.md
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* Add feature map to save npy files
Add feature map to save npy files,export npy files with 32 feature maps per layer.
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* Update plots.py
* Update plots.py
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* Update plots.py
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* prune unused imports
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* handle exceptions| attempt CI
* update
* Pre-commit manual run
* yaml one-liner
* Update ci-testing.yml
* Comment W&B CI
Leave as example for future separate CI
* Update ci-testing.yml
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* AutoBatch, AutoAnchor `LOGGER`
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* Update autoanchor.py
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* Improve plots.py robustness
Addresses issues #5374, #5395, #5611
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* Fix to resource warning allocation; utilize file.open within a context manager
* rename fh to f
in keeping with naming convention
Co-authored-by: Ayman Saleh <aymansaleh@Aymans-MacBook-Pro-2.local>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update __init__.py
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* notebook_init
* notebook_init
* notebook_init
* notebook_init
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* notebook_init
* Created using Colaboratory
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* Logger consolidation
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* Fix `save_one_box()`
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Glenn Jocher
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