* set common default stride as 32
* restore default stride, and set it on argument optional
* fix wrong use of opt
* fix missing parameter of stride
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* precommit: yapf
* align isort
* fix
# Conflicts:
# utils/plots.py
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* refactor: use edgetpu flag
* fix: remove bitwise and assignation to tflite
* Cleanup and fix tflite
* Cleanup
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* Fix CoreML P6 inference
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* Fix exporting saved_model: pb exceeds 2GB
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* Clean up
* Remove lambda in tf.function()
* Revert "Remove lambda in tf.function()" to be compatible with TF v2.4
This reverts commit 46c7931f11dfdea6ae340c77287c35c30b9e0779.
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* Edge TPU `tf.lite.experimental.load_delegate` fix
Fix attempt for #6535
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* import tflite_runtime if tensorflow not installed
* rename tflite to tfli
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Also rename tfli to tfl
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* 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
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* Fix saved_model and pb detect error
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* Add edgetpu in export.py docstring
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* 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
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* Check TensorRT>=8.0.0 version
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* Fix tolist() to add the file for each Detection
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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
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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
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* Logger consolidation
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* Fix `save_one_box()`
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* precommit: isort
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* fix different devices bug
* extend _apply() instead of to() for a general fix
* Only apply if Detect() is last layer
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* Indent fix
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* Added max_det parameters in various places
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* Update inference default to 1000 instances
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* rename class autoShape -> AutoShape
follow other class naming convention
* rename class autoShape -> AutoShape
follow other classes' naming convention
* rename class autoShape -> AutoShape
PR https://github.com/ultralytics/yolov5/pull/2725 introduced a very specific bug that only affects multi-GPU trainings. Apparently the cause was using the torch.cuda.amp decorator in the autoShape forward method. I've implemented amp more traditionally in this PR, and the bug is resolved.
* yolotr
* transformer block
* Remove bias in Transformer
* Remove C3T
* Remove a deprecated class
* put the 2nd LayerNorm into the 2nd residual block
* move example model to models/hub, rename to -transformer
* Add module comments and TODOs
* Remove LN in Transformer
* Add comments for Transformer
* Solve the problem of MA with DDP
* cleanup
* cleanup find_unused_parameters
* PEP8 reformat
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Glenn Jocher
rglkt
Jirka Borovec
Mrinal Jain
DavidB
Raffaele Galliera
Jiacong Fang
Motoki Kimura
Yono Mittlefehldt
Li Zeng
imyhxy
Phil2020
Ding Yiwei
Jebastin Nadar
Jean-Baptiste Martin
ELHoussineT
Yonghye Kwon
jmiranda-laplateforme
Valentin Aliferov
Adrian Holovaty
Burhan