* Minor fixes of the git checkout new branch
* Use em dash to quote
* Revert the change of git checkout
* Maybe we should up-to-date with the upstream/master?
* Add nano model to the download list
* Add possibility to also download the "*6" models
* Cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* take EXIF orientation tags into account when fixing corrupt images
* fit 120 char
* sort imports
* Update local exif_transpose comment
We have a local inplace version that is faster than the official as the image is not copied. AutoShape() uses this for Hub models, but here it is not important as the datasets.py usage is infrequent (AutoShape() it is applied every image).
* Update datasets.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* take image files with uppercase extensions into account in autosplit
* case fix
* Refactor implementation
Removes additional variable (capital variable names are also only for global variables), and uses the same methodology as implemented earlier in datasets.py L409.
* Remove redundant rglob characters
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Autofix duplicate labels
PR changes duplicate label handling from report error and ignore image-label pair to report warning and autofix image-label pair.
This should fix this common issue for users and allow everyone to get started and get a model trained faster and easier than before.
* sign fix
* Cleanup
* Increment cache version
* all to any fix
* Update Objects365.yaml
Download the official Objects365 validation set and convert the labels
* Enforce 4-space indent, reformat and cleanup
* shorten list comprehension
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Add train class filter feature to datasets.py
Allows for training on a subset of total classes if `include_class` list is defined on datasets.py L448:
```python
include_class = [] # filter labels to include only these classes (optional)
```
* segments fix
* update detect.py in order to support torch script
This change assumes the torchscrip file was previously saved with `export.py`
* update `detect.py` for torchscript support
Simple update for torchscript support. Assumes the torchscript file has been generated with `export.py`
* Cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* use os.path.relpath instead of relative_to
* use os.path.relpath instead of relative_to
* Remove os.path from val.py
* Remove os.path from train.py
* Update detect.py import to os
* Update export.py import to os
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* fix different devices bug
* extend _apply() instead of to() for a general fix
* Only apply if Detect() is last layer
Co-authored-by: Jebastin Nadar <njebastin10@gmail.com>
* Indent fix
* Add comment to yolo.py
* Add comment to common.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Requirements updated
1. Requests added to requirements.txt. That might not be included in all docker base images, adding it to the requirements is safer.
2. Added a minimum version to Pandas. It's a good practice to have versions for all dependencies.
* Sort alphabetically
* Update requirements.txt
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This PR adds a new training argument `--save-period` to save training checkpoints every `x` epochs. To save training every 50 epochs for example:
```
python train.py --save-period 50 # saves epoch50.pt, epoch100.pt, epoch150.pt, ... etc.
```
This saved checkpoints in addition to existing last.pt and best.pt checkpoints and does not affect their behavior. Default value is -1, i.e. disabled.
Ayush Chaurasia
Zhiqiang Wang
Cristi Fati
Jebastin Nadar
Glenn Jocher
jdfr
dependabot[bot]
Zhiqiang Wang
Farley Lai
NauchtanRobotics
Yoni Chechik
Andrei Ionut Damian
Malte Lorbach
Nan
hamid mohammadi
Egor Orel