* 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>
21.12 generates dockerhub errors so rolling back to 21.10 with latest pytorch install. Not sure if this torch install will work on non-GPU dockerhub autobuild so this is an experiment.
* `export.py` automatic `forward_export`
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Enable ONNX ``--half` FP16 inference
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add models/tf.py for TensorFlow and TFLite export
* Set auto=False for int8 calibration
* Update requirements.txt for TensorFlow and TFLite export
* Read anchors directly from PyTorch weights
* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export
* Remove check_anchor_order, check_file, set_logging from import
* Reformat code and optimize imports
* Autodownload model and check cfg
* update --source path, img-size to 320, single output
* Adjust representative_dataset
* Put representative dataset in tfl_int8 block
* detect.py TF inference
* weights to string
* weights to string
* cleanup tf.py
* Add --dynamic-batch-size
* Add xywh normalization to reduce calibration error
* Update requirements.txt
TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error
* Fix imports
Move C3 from models.experimental to models.common
* Add models/tf.py for TensorFlow and TFLite export
* Set auto=False for int8 calibration
* Update requirements.txt for TensorFlow and TFLite export
* Read anchors directly from PyTorch weights
* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export
* Remove check_anchor_order, check_file, set_logging from import
* Reformat code and optimize imports
* Autodownload model and check cfg
* update --source path, img-size to 320, single output
* Adjust representative_dataset
* detect.py TF inference
* Put representative dataset in tfl_int8 block
* weights to string
* weights to string
* cleanup tf.py
* Add --dynamic-batch-size
* Add xywh normalization to reduce calibration error
* Update requirements.txt
TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error
* Fix imports
Move C3 from models.experimental to models.common
* implement C3() and SiLU()
* Add TensorFlow and TFLite Detection
* Add --tfl-detect for TFLite Detection
* Add int8 quantized TFLite inference in detect.py
* Add --edgetpu for Edge TPU detection
* Fix --img-size to add rectangle TensorFlow and TFLite input
* Add --no-tf-nms to detect objects using models combined with TensorFlow NMS
* Fix --img-size list type input
* Update README.md
* Add Android project for TFLite inference
* Upgrade TensorFlow v2.3.1 -> v2.4.0
* Disable normalization of xywh
* Rewrite names init in detect.py
* Change input resolution 640 -> 320 on Android
* Disable NNAPI
* Update README.me --img 640 -> 320
* Update README.me for Edge TPU
* Update README.md
* Fix reshape dim to support dynamic batching
* Fix reshape dim to support dynamic batching
* Add epsilon argument in tf_BN, which is different between TF and PT
* Set stride to None if not using PyTorch, and do not warmup without PyTorch
* Add list support in check_img_size()
* Add list input support in detect.py
* sys.path.append('./') to run from yolov5/
* Add int8 quantization support for TensorFlow 2.5
* Add get_coco128.sh
* Remove --no-tfl-detect in models/tf.py (Use tf-android-tfl-detect branch for EdgeTPU)
* Update requirements.txt
* Replace torch.load() with attempt_load()
* Update requirements.txt
* Add --tf-raw-resize to set half_pixel_centers=False
* Remove android directory
* Update README.md
* Update README.md
* Add multiple OS support for EdgeTPU detection
* Fix export and detect
* Export 3 YOLO heads with Edge TPU models
* Remove xywh denormalization with Edge TPU models in detect.py
* Fix saved_model and pb detect error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix pre-commit.ci failure
* Add edgetpu in export.py docstring
* Fix Edge TPU model detection exported by TF 2.7
* Add class names for TF/TFLite in DetectMultibackend
* Fix assignment with nl in TFLite Detection
* Add check when getting Edge TPU compiler version
* Add UTF-8 encoding in opening --data file for Windows
* Remove redundant TensorFlow import
* Add Edge TPU in export.py's docstring
* Add the detect layer in Edge TPU model conversion
* Default `dnn=False`
* Cleanup data.yaml loading
* Update detect.py
* Update val.py
* Comments and generalize data.yaml names
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: unknown <fangjiacong@ut.cn>
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* 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
Glenn Jocher
Matthias
Ayush Chaurasia
sitecao
imyhxy
johnk2hawaii
Otfot
Henry
Jinwoong Yoo
Yin Rong
pre-commit-ci[bot]
bilzard
Jiacong Fang
Chen Gen
Awsaf