* Auto TFLite uint8 detection
This PR automatically determines if TFLite models are uint8 quantized rather than accepting a manual argument.
The quantization determination is based on @zldrobit comment https://github.com/ultralytics/yolov5/pull/1127#issuecomment-901713847
* Cleanup
* 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>
* Added the recording feature for multiple streams
Thanks for the very cool repo!!
I was trying to record multiple feeds at the same time, but the current version of the detector only had one video writer and one vid_path!
So the streams were not being saved and only were initialized with one frame and this process didn't record the whole thing.
Fix:
I made a list of `vid_writer` and `vid_path` and the `i` from the loop over the `pred` took care of the writer which need to work!
I hope this helps, Thanks!
* Cleanup list lengths
* batch size variable
* Update datasets.py
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>
* Added max_det parameters in various places
* 120 character line
* PEP8
* 120 character line
* Update inference default to 1000 instances
* Update inference default to 1000 instances
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* command line option for line thickness and hiding labels
* command line option for line thickness and hiding labels
* command line option for line thickness and hiding labels
* command line option for line thickness and hiding labels
* command line option for line thickness and hiding labels
* command line option for hiding confidence values
* Update detect.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This updates the default detect.py behavior to automatically save all inference images/videos/webcams unless the new argument --nosave is used (python detect.py --nosave) or unless a list of streaming sources is passed (python detect.py --source streams.txt)
* Adding --output and --save-conf options to test.py
* Update help fields
* Update test.py
* Make arguments and comments uniform with test.py
* Remove previous and print save_dir on finish
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* add contrast to conf/class against bbox rectangle color
* added feature to save conf in txt labels
* Update general.py
* Update detect.py
* Update detect.py
* Update detect.py
save_conf variable removed as only used once, so we can use opt.save_conf instead.
* Update detect.py
* Update detect.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Add logging setup
* Fix fusing layers message
* Fix logging does not have end
* Add logging
* Change logging to use logger
* Update yolo.py
I tried this in a cloned branch, and everything seems to work fine
* Update yolo.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update detect.py
Update print info for relative path and absolute
otherwise the info below is misleading
Results saved to /workspace//output_path
* Update detect.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
In torch==1.5, the import of the API has changed. Although it does not interrupt the operation of the program, it seems to me to be an implicit error and may throw an exception in later versions.
Glenn Jocher
Jiacong Fang
ketan-b
lb-desupervised
Adrian Holovaty
albinxavi
Maximilian Peters
fcakyon
Burhan
Ben Milanko
ab-101
Rohan Dubey
weida wang
Khiem Doan
oleg
Osama Akhtar
Frederik
stone99
NanoCode012
hopesala
lorenzomammana
Jirka Borovec
Russ Ferriday
Anish Hiranandani
Lornatang