* 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
* make parameter ignore epochs
ignore epochs functionality add to prevent spikes at the beginning when fitness spikes and decreases after.
Discussed at https://github.com/ultralytics/yolov5/issues/5971
* Update train.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Fix tolist() to add the file for each Detection
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* Fix PEP8 requirement for 2 spaces before an inline comment
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* Cleanup
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* fix .gitignore not tracking existing folders
fix .gitignore so that the files that are in the repository are actually being tracked.
Everything in the data/ folder is ignored, which also means the subdirectories are ignored. Fix so that the subdirectories and their contents are still tracked.
* Remove data/trainings
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* 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>
* Update val.py
Add an option to choose number of workers if not called by train.py
* Update comment
* 120 char line width
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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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* tensorflow or tflite exclusively as interpreter
As per bug report https://github.com/ultralytics/yolov5/issues/5709 I think there should be only one attempt to assign interpreter, and it appears tflite is only ever needed for the case of edgetpu model.
* Scope imports
* Nested definition line fix
* Update common.py
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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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* Export and detect with TensorRT engine file
* Resolve `isort`
* Make validation works with TensorRT engine
* feat: update export docstring
* feat: change suffix from *.trt to *.engine
* feat: get rid of pycuda
* feat: make compatiable with val.py
* feat: support detect with fp16 engine
* Add Lite to Edge TPU string
* Remove *.trt comment
* Revert to standard success logger.info string
* Fix Deprecation Warning
```
export.py:310: DeprecationWarning: Use build_serialized_network instead.
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
```
* Revert deprecation warning fix
@imyhxy it seems we can't apply the deprecation warning fix because then export fails, so I'm reverting my previous change here.
* Update export.py
* Update export.py
* Update common.py
* export onnx to file before building TensorRT engine file
* feat: triger ONNX export failed early
* feat: load ONNX model from file
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* 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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Glenn Jocher
Mrinal Jain
jinmc
Diego Montes
Yono Mittlefehldt
iumyx2612
Pascal Maillard
Felix You
greg2451
Ayush Chaurasia
Li Zeng
Can
Vishnu Pradeep
gggmt
Yu Zhang
imyhxy
dependabot[bot]
Phil2020
rockstarr
Zengyf-CVer
IL2006
Jirka Borovec