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* Implement DDP `static_graph=True`
Experimental implementation of new PyTorch 1.11.0 DDP feature.
* Add 1.11.0 check
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* PyTorch Hub `_verbose=False` fix2
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* support nomedia
* support nomedia for validation
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* Update train.py
* Revert no plot evolve
evolve plots do not contain any images
* Revert plot_results
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* Add support for different normalization layers.
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* Print dataset scan only `if RANK in (-1, 0)`
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* precommit: yapf
* align isort
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# Conflicts:
# utils/plots.py
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New `--cache val` argument will cache validation set only into RAM. Should help multi-GPU training speeds without consuming as much RAM as full `--cache ram`.
* Update train.py
As see in #6463, modification on train in evolve process to allow custom save directory.
* fix val
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* New flag 'stop_training' in util.callbacks.Callbacks class to prematurely stop training from callback handler
* Removed most of the new checks, leaving only the one after calling 'on_train_batch_end'
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* Remove NCOLS from tqdm
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* update
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* Logger consolidation
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* write date in checkpoint file
write date in checkpoint file
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* precommit: isort
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* use os.path.relpath instead of relative_to
* use os.path.relpath instead of relative_to
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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.
* Validate best.pt on train end
* 0.7 iou for COCO only
* pass callbacks
* active model.float() if not half
* print Validating best.pt...
* add newline
* 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
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* 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
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* Cleanup
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Glenn Jocher
Ayush Chaurasia
Vardan Agarwal
Jirka Borovec
Christoph Fürbacher
Matthias Vadcard
bilzard
Matthias
Otfot
Felix You
Werner Duvaud
Yonghye Kwon
Malte Lorbach
Nan
Zhiqiang Wang
Kalen Michael
imyhxy
junji hashimoto
IneovaAI