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* Fix exporting saved_model: pb exceeds 2GB

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* Add benchmarks.py

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* Edge TPU TF imports fix

Fix for https://github.com/ultralytics/yolov5/issues/6535#issuecomment-1030631526

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* Edge TPU `tf.lite.experimental.load_delegate` fix

Fix attempt for #6535

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* Add models/tf.py for TensorFlow and TFLite export

* Set auto=False for int8 calibration

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* Read anchors directly from PyTorch weights

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* update --source path, img-size to 320, single output

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* detect.py TF inference

* weights to string

* weights to string

* cleanup tf.py

* Add --dynamic-batch-size

* Add xywh normalization to reduce calibration error

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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

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* Put representative dataset in tfl_int8 block

* weights to string

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* 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

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* Fix reshape dim to support dynamic batching

* Fix reshape dim to support dynamic batching

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* Add get_coco128.sh

* Remove --no-tfl-detect in models/tf.py (Use tf-android-tfl-detect branch for EdgeTPU)

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* Replace torch.load() with attempt_load()

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* Add --tf-raw-resize to set half_pixel_centers=False

* Remove android directory

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* Add multiple OS support for EdgeTPU detection

* Fix export and detect

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* Default `dnn=False`

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* Check TensorRT>=8.0.0 version

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* Add ONNX inference providers

Fix for https://github.com/ultralytics/yolov5/issues/5916

* Update common.py

* 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.

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* Export and detect with TensorRT engine file

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* feat: support detect with fp16 engine

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```
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

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* 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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* New `DetectMultiBackend()` class

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* Fix `save_one_box()`

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* precommit: isort

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