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)
* Update hubconf.py with check_requirements()
Dependency checks have been missing from YOLOv5 PyTorch Hub model loading, causing errors in some cases when users are attempting to import hub models in unsupported environments. This should examine the YOLOv5 requirements.txt file and pip install any missing or version-conflict packages encountered.
This is highly experimental (!), please let us know if this creates problems in your custom workflows.
* Update hubconf.py
* Update check_requirements() with auto-install
This PR builds on an idea I had to automatically install missing dependencies rather than simply report an error message.
YOLOv5 should now 1) display all dependency issues and not simply display the first missing dependency, and 2) attempt to install/update each missing/VersionConflict package.
* cleanup
* cleanup 2
* Check requirements.txt file exists
* cleanup 3
* Be able to create dataset from annotated images only
Add the ability to create a dataset/splits only with images that have an annotation file, i.e a .txt file, associated to it. As we talked about this, the absence of a txt file could mean two things:
* either the image wasn't yet labelled by someone,
* either there is no object to detect.
When it's easy to create small datasets, when you have to create datasets with thousands of images (and more coming), it's hard to track where you at and you don't want to wait to have all of them annotated before starting to train. Which means some images would lack txt files and annotations, resulting in label inconsistency as you say in #2313. By adding the annotated_only argument to the function, people could create, if they want to, datasets/splits only with images that were labelled, for sure.
* Cleanup and update print()
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* option for skip last layer and cuda export support
* added parameter device
* fix import
* cleanup 1
* cleanup 2
* opt-in grid
--grid will export with grid computation, default export will skip grid (same as current)
* default --device cpu
GPU export causes ONNX and CoreML errors.
Co-authored-by: Jan Hajek <jan.hajek@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* faster random index generator for mosaic augementation
We don't need to access list to generate random index
It makes augmentation slower.
* Update datasets.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>