* 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>
* 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>
* precommit: isort
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update isort config
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update name
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>
* use os.path.relpath instead of relative_to
* use os.path.relpath instead of relative_to
* Remove os.path from val.py
* Remove os.path from train.py
* Update detect.py import to os
* Update export.py import to os
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Changed onnx-simplifier check behavior
Export.py has been updated to check for onnx-simplifier requirement only when the --simplify argument is added.
Allows for better flexibility and one less requirement if simplify is not needed.
* Fix single-element tuples
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Add cache-on-disk and cache-directory to cache images on disk
* Fix load_image with cache_on_disk
* Add no_cache flag for load_image
* Revert the parts('logging' and a new line) that do not need to be modified
* Add the assertion for shapes of cached images
* Add a suffix string for cached images
* Fix boundary-error of letterbox for load_mosaic
* Add prefix as cache-key of cache-on-disk
* Update cache-function on disk
* Add psutil in requirements.txt
* Update train.py
* Cleanup1
* Cleanup2
* Skip existing npy
* Include re-space
* Export return character fix
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Slightly modify CLI execution
This simple change makes it easier to run the primary functions of this
repo (train/detect/test) from within Python. An object which represents
`opt` can be constructed and fed to the `main` function of each of these
modules, rather than having to call the lower level functions directly,
or run the module as a script.
* Update export.py
Add CLI parsing update for more convenient module usage within Python.
Co-authored-by: Lewis Belcher <lb@desupervised.io>
* call model.eval() when opt.train is False
call model.eval() when opt.train is False
* single-line if statement
* cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Add output names & dynamic axes for onnx export
Add output_names and dynamic_axes names for all outputs in torch.onnx.export. The first four outputs of the model will have names output0, output1, output2, output3
* use first output only + cleanup
Co-authored-by: Samridha Shrestha <samridha.shrestha@g42.ai>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Jiacong Fang
Glenn Jocher
Diego Montes
imyhxy
rockstarr
Jirka Borovec
Malte Lorbach
Kalen Michael
Zegorax
junji hashimoto
fcakyon
lb-desupervised
Yonghye Kwon
Sam_S
WangChaofeng
Cristi Fati