* OpenVINO export

* Remove timeout

* Add 3 files

* str

* Constrain opset to 12

* Default ONNX opset to 12

* Make dir

* Make dir

* Cleanup

* Cleanup

* check_requirements(('openvino-dev',))

* Requirements updated

1. Requests added to requirements.txt. That might not be included in all docker base images, adding it to the requirements is safer.
2. Added a minimum version to Pandas. It's a good practice to have versions for all dependencies.

* Sort alphabetically

* Update requirements.txt

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Unpin Pillow

* Update requirements.txt

* Update plots.py

* Initial commit

* Remove unused export_torchscript return

* ROOT variable

* Add prefix to fcn arg

* fix ROOT

* check_yaml into run()

* interim fixes

* imgsz=(320, 320)

* Hardcode tf_raw_resize False

* Finish opt elimination

* Update representative_dataset_gen()

* Update export.py with TF methods

* SiLU and GraphDef fixes

* file_size() directory handling feature

* export fixes

* add lambda: to representative_dataset

* Detect training False default

* Fuse false for TF models

* Embed agnostic NMS arguments

* Remove lambda

* TensorFlow.js export success

* Add pb to Usage

* Add *_tfjs_model/ to ignore files

* prepend YOLOv5 to function headers

* Remove end --- comments

* parameterize tfjs export pb file

* update run() data default /ROOT

* update --include help

* update imports

* return ct_model

* Consolidate TFLite export

* pb prerequisite to tfjs

* TF modules CamelCase

* Remove exports from tf.py and cleanup

* pass agnostic NMS arguments

* CI

* CI

* ignore *_web_model/

* Add tensorflow to CI dependencies

* CI tensorflow-cpu

* Update requirements.txt

* Remove tensorflow check_requirement

* CI coreml tfjs

* export only onnx torchscript

* reorder exports torchscript first

* Add Annotator() class

* Download Arial

* 2x for loop

* Cleanup

* tuple 2 list

* max_size=1920

* bold logging results to

* tolist()

* im = annotator.im

* PIL save in detect.py

* Smart asarray in detect.py

* revert to cv2.imwrite

* Cleanup

* Return result asarray

* Add `Profile()` profiler

* CamelCase Timeout

* Resize after mosaic

* pillow>=8.0.0

* daemon imwrite

* Add cv2 support

* Remove plot_wh_methods and plot_one_box

* pil=False for hubconf.py annotations

* im.shape bug fix

* colorstr common.py

* join daemons

* Update t.daemon

* Removed daemon saving

* 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()

* 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

* Add --agnostic-nms for TF class-agnostic NMS

* Cleanup after merge

* Cleanup2 after merge

* Cleanup3 after merge

* Add tf.py docstring with credit and usage

* pb saved_model and tflite use only one model in detect.py

* Add use cases in docstring of tf.py

* Remove redundant `stride` definition

* Remove keras direct import

* Fix `check_requirements(('tensorflow>=2.4.1',))`

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Albumentations integration

* ToGray p=0.01

* print confirmation

* create instance in dataloader init method

* improved version handling

* transform not defined fix

* assert string update

* create check_version()

* add spaces

* update class comment

* Update README.md

* Changing FLOPS to FLOPs.

Co-authored-by: BuildTools <unconfigured@null.spigotmc.org>

Ultralytics REST API fix.

Cython should be a dependency of the remaining packages in requirements.txt, so should be installed anyway even if not a direct requirement.

* Update requirements.txt

* Update ci-testing.yml

* Update hubconf.py

* Simplify autoshape() post-process

* cleanup

* cleanup

* initial commit

* add plotting

* matrix to cpu

* bug fix

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* seaborn pandas to requirements.txt

* seaborn pandas to requirements.txt

* update wandb plotting

* remove pandas

* if plots

* if plots

* if plots

* if plots

* if plots

* initial commit

* add plotting

* matrix to cpu

* bug fix

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* update plot

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* cleanup

* seaborn pandas to requirements.txt

* seaborn pandas to requirements.txt

* update wandb plotting

* remove pandas

* if plots

* if plots

* if plots

* if plots

* if plots

* Cat apriori to autolabels

* cleanup

* Add wandb metric logging and bounding box debugging

* Improve formatting, readability

* Remove mutliple path for init, improve formatting

* Add wandb params

* Remove typecasting in bbox coordinates and reformat

* Cleanup

* add wandb to requirements.txt

* minor updates to test.py

* general reorg

* reduce --log-imgs to 10

* clean wandb import

* reverse wandb import assert

* add except AssertionError to try import

* move wandb init to all global ranks

* replace print() with logger.info()

* replace print() with logger.info()

* move wandb.init() bug fix

* project PosixPath to basename bug fix

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update ci-testing.yml

* Update ci-testing.yml

* Update requirements.txt

* Update requirements.txt

* Update google_utils.py

* Update test.py

* Update ci-testing.yml

numpy 1.17.3 is required for python3.8

for smoothly export model to onnx, onnx should be 1.6.0