* 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
for more information, see https://pre-commit.ci
* 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>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix PEP8 requirement for 2 spaces before an inline comment
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Cleanup
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* 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
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.
Jiacong Fang
Chen Gen
Awsaf
Ayush Chaurasia
Deep Patel
JieLi
Glenn Jocher
Felix You
Mrinal Jain
jinmc
Diego Montes
Yono Mittlefehldt
iumyx2612
Pascal Maillard
greg2451
Li Zeng
Can
Vishnu Pradeep
gggmt
Yu Zhang
imyhxy