Auto-fix corrupt JPEGs PR introduced a bug whereby the f.seek() operation read all of the bytes in the image, resulting in the PIL image having nothing to read upon the .save() operation.
Fix was to re-open the image using PIL before saving.
* Autofix corrupt JPEGs
This PR automatically re-saves corrupt JPEGs and trains with the resaved images. WARNING: this will overwrite the existing corrupt JPEGs in a dataset and replace them with correct JPEGs, though the filesize may increase and the image contents may not be exactly the same due to lossy JPEG compression schemes. Results may vary by JPEG decoder and hardware.
Current behavior is to exclude corrupt JPEGs from training with a warning to the user, but many users have been complaining about large parts of their dataset being excluded from training.
* Clarify re-save reason
* 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>
* 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>
* Add freeze as an argument
I train on different platforms and sometimes I want to freeze some layers. I have to go into the code and change it and also keep track of how many layers I froze on what platform. Please add the number of layers to freeze as an argument in future versions thanks.
* Update train.py
* Update train.py
* Cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Glenn Jocher
Ayush Chaurasia
Huu Quan, CAP
Jiacong Fang
Omid Sadeghnezhad
Ahmad Mustafa Anis
Yuantao Yang
Yonghye Kwon
Sudhanshu Singh
imyhxy
junji hashimoto
Kalen Michael
IneovaAI