Yolov5

Latest version: v7.0.13

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6.1.2

What's Changed
* update to 28.04.22 ultralytics/yolov5 by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/110


**Full Changelog**: https://github.com/fcakyon/yolov5-pip/compare/6.1.1...6.1.2

6.1.1

What's Changed
* update multibackend model load by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/104
* fix: disable usage of root logger by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/105
* improve image size arg by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/107
* fix tensorrt inference by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/108
* update pretrained model release tag by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/109


**Full Changelog**: https://github.com/fcakyon/yolov5-pip/compare/6.1.0...6.1.1

6.1

[assets]: https://github.com/ultralytics/yolov5/releases
[previous]: https://github.com/ultralytics/yolov5/releases/tag/v6.0
[current]: https://github.com/ultralytics/yolov5/releases/tag/v6.1
[TTA]: https://github.com/ultralytics/yolov5/issues/303

This release incorporates many new features and bug fixes ([**271 PRs** from **48 contributors**](https://github.com/ultralytics/yolov5/compare/v6.0...v6.1)) since our last [release][previous] in October 2021. It adds [TensorRT](https://github.com/ultralytics/yolov5/pull/5699), [Edge TPU](https://github.com/ultralytics/yolov5/pull/3630) and [OpenVINO](https://github.com/ultralytics/yolov5/pull/6057) support, and provides retrained models at `--batch-size 128` with new default one-cycle linear LR [scheduler](https://github.com/ultralytics/yolov5/pull/6729). YOLOv5 now officially supports 11 different formats, not just for export but for inference (both detect.py and PyTorch Hub), and validation to profile mAP and speed results after export.

Format | `export.py --include` | Model
:--- | --: | :--
[PyTorch](https://pytorch.org/) | - | `yolov5s.pt`
[TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov5s.torchscript`
[ONNX](https://onnx.ai/) | `onnx` | `yolov5s.onnx`
[OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov5s_openvino_model/`
[TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov5s.engine`
[CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov5s.mlmodel`
[TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov5s_saved_model/`
[TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov5s.pb`
[TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov5s.tflite`
[TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov5s_edgetpu.tflite`
[TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov5s_web_model/`


Usage examples (ONNX shown):
bash
Export: python export.py --weights yolov5s.pt --include onnx
Detect: python detect.py --weights yolov5s.onnx
PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Validate: python val.py --weights yolov5s.onnx
Visualize: https://netron.app



Important Updates

- **TensorRT support**: TensorFlow, Keras, TFLite, TF.js model export now fully integrated using `python export.py --include saved_model pb tflite tfjs` (https://github.com/ultralytics/yolov5/pull/5699 by imyhxy)
- **Tensorflow Edge TPU support ⭐ NEW**: New smaller YOLOv5n (1.9M params) model below YOLOv5s (7.5M params), exports to 2.1 MB INT8 size, ideal for ultralight mobile solutions. (https://github.com/ultralytics/yolov5/pull/3630 by zldrobit)
- **OpenVINO support**: YOLOv5 ONNX models are now compatible with both OpenCV DNN and ONNX Runtime (https://github.com/ultralytics/yolov5/pull/6057 by glenn-jocher).
- **Export Benchmarks**: Benchmark (mAP and speed) all YOLOv5 export formats with `python utils/benchmarks.py --weights yolov5s.pt`. Currently operates on CPU, future updates will implement GPU support. (https://github.com/ultralytics/yolov5/pull/6613 by glenn-jocher).
- **Architecture:** no changes
- **Hyperparameters:** minor change
- hyp-scratch-large.yaml `lrf` reduced from 0.2 to 0.1 (https://github.com/ultralytics/yolov5/pull/6525 by glenn-jocher).
- **Training:** Default Learning Rate (LR) scheduler updated
- One-cycle with cosine replace with one-cycle linear for improved results (https://github.com/ultralytics/yolov5/pull/6729 by glenn-jocher).

New Results

All model trainings logged to https://wandb.ai/glenn-jocher/YOLOv5_v61_official

<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
<details>
<summary>YOLOv5-P5 640 Figure (click to expand)</summary>

<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
</details>
<details>
<summary>Figure Notes (click to expand)</summary>

* **COCO AP val** denotes mAP0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
</details>

Example YOLOv5l before and after metrics:

|YOLOv5l<br><sup>Large|size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup> 640 (B)
--- |--- |--- |--- |--- |--- |--- |--- |---

6.1.0

This yolov5 package contains everything from ultralytics/yolov5 [at this commit](https://github.com/ultralytics/yolov5/commit/ea72b84f5e690cb516642ce2d9ae200145b0af34) plus:
1. Easy installation via pip: `pip install yolov5`
2. Full CLI integration with [Fire](https://github.com/google/python-fire) package
3. NeptuneAI logger support (metric, model and dataset logging)
4. S3 support (model and dataset upload)
5. Classwise AP logging during experiment
6. COCO dataset format support (for training)

What's Changed
* update to ultralytics/yolov5 04.04.22 by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/90
* delete duplicate data config by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/91
* update test for latest model weights by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/92


**Full Changelog**: https://github.com/fcakyon/yolov5-pip/compare/6.0.7...6.1.0

6.0.7

What's Changed
* add torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/74
* update to v6.0.7 by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/75
* fix check_version by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/76
* Update README.md by 5a7man in https://github.com/fcakyon/yolov5-pip/pull/82

New Contributors
* 5a7man made their first contribution in https://github.com/fcakyon/yolov5-pip/pull/82

**Full Changelog**: https://github.com/fcakyon/yolov5-pip/compare/6.0.6...6.0.7

6.0.6

What's Changed
* fix neptune logging by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/69
* reformat codebase with isort by fcakyon in https://github.com/fcakyon/yolov5-pip/pull/71


**Full Changelog**: https://github.com/fcakyon/yolov5-pip/compare/6.0.5...6.0.6

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