* Add `psutil` and `ipython` to requirements.txt
Lightweight packages used by YOLOv5 for system utilization (psutil) and interactive notebooks (IPython)
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* sort alphabetically
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
* Write .yaml file when exporting model to openvino
Write .yaml file automatically when exporting model to openvino to be used during inference
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* Update export.py
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* Code refactor for general.py
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* Add PyTorch AMP check
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* Cleanup
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* Robust for DDP
* Fixes
* Add amp enabled boolean to check_train_batch_size
* Simplify
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* Add DWConvTranspose2d() module
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* Add DWConvTranspose2d() module
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* Fix
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* Improve mAP0.5-0.95
Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function
Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.
Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s]
all 5000 36335 0.743 0.626 0.682 0.506
from pycocotools
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685
These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.
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* Remove line to retain pycocotools results
* Update val.py
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* Removed shell=True from subprocess commands that require user inputs. Also removed unused arguments
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* Added check=True
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* Create docker.yml
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* Cleanup
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* Uses OpenVINO runtime instead of openvino-dev
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* export with openvino package
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* Revert export.py
* Update common.py
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* New TensorFlow `TFCrossConv()` module
* Move from experimental to common
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* Add C3x
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* TFC3x bug fix
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* New TensorFlow `TFDWConv()` module
Analog to DWConv() module:
8aa2085a7e/models/common.py (L53-L57)
* Fix and new activations() function
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* Add `@threaded` decorator
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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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* Pin downloads to release version
Fixes a release version to avoid forward-compatibility issues in future releases.
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* Ability to dowlnoad older assets
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* Cleanup
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