# yolov5-6.0-plus **Repository Path**: luokai-dandan/yolov5-6.0-plus ## Basic Information - **Project Name**: yolov5-6.0-plus - **Description**: 重新改写u版yolov5 6.0中的detect.py文件，封装为可调用的函数，开发detect-qt，加入自定义检测禁区、节流和RTSP推流功能，后续加入改写模型为TensorRT，加快推理速度。 - **Primary Language**: Python - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-11-01 - **Last Updated**: 2024-04-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

Documentation

See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. ##

Quick Start Examples

Install

[**Python>=3.6.0**](https://www.python.org/) is required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/): ```bash $ git clone https://github.com/ultralytics/yolov5 $ cd yolov5 $ pip install -r requirements.txt ```

Inference

Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). ```python import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom # Images img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list # Inference results = model(img) # Results results.print() # or .show(), .save(), .crop(), .pandas(), etc. ```

Inference with detect.py

`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash $ python detect_ui.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/NUsoVlDFqZg' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```

Training

Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). ```bash $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 yolov5m 40 yolov5l 24 yolov5x 16 ```

Tutorials

* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED * [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️ RECOMMENDED * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW * [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) * [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW * [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 * [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) * [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) * [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) * [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) * [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW * [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)

Environments

Get started in seconds with our verified environments. Click each icon below for details.

Integrations

|Weights and Biases|Roboflow ⭐ NEW| |:-:|:-:| |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and automatically export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | ##

Why YOLOv5

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)

* **COCO AP val** denotes mAP@0.5:0.95 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`

### Pretrained Checkpoints [assets]: https://github.com/ultralytics/yolov5/releases [TTA]: https://github.com/ultralytics/yolov5/issues/303 |Model |size
^{(pixels) |mAP^{val
0.5:0.95 |mAP^{val
0.5 |Speed
^{CPU b1
(ms) |Speed
^{V100 b1
(ms) |Speed
^{V100 b32
(ms) |params
^{(M) |FLOPs
^{@640 (B)
|--- |--- |--- |--- |--- |--- |--- |--- |---
|[YOLOv5n][assets] |640 |28.4 |46.0 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
|[YOLOv5s][assets] |640 |37.2 |56.0 |98 |6.4 |0.9 |7.2 |16.5
|[YOLOv5m][assets] |640 |45.2 |63.9 |224 |8.2 |1.7 |21.2 |49.0
|[YOLOv5l][assets] |640 |48.8 |67.2 |430 |10.1 |2.7 |46.5 |109.1
|[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7
| | | | | | | | |
|[YOLOv5n6][assets] |1280 |34.0 |50.7 |153 |8.1 |2.1 |3.2 |4.6
|[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |16.8 |12.6
|[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0
|[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.8 |111.4
|[YOLOv5x6][assets]
+ [TTA][TTA]|1280
1536 |54.7
**55.4** |**72.4**
72.3 |3136
- |26.2
- |19.4
- |140.7
- |209.8
-

Table Notes (click to expand)

* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
* **mAP^val** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`

## Contribute

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see
our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out
the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to provide
thoughts and feedback on your experience with YOLOv5. Thank you!

## Contact

For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).}}}}}}}}