qt5_yolov3

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## 重要

下载权重，权重文件请放在  weights/yolov3.pt  下载链接 链接：[https://pan.baidu.com/s/1Da0CuJqx2Zv5bJdss-wv-Q?pwd=chen](https://pan.baidu.com/s/1Da0CuJqx2Zv5bJdss-wv-Q?pwd=chen) 
提取码：chen

打开`ui_yolov3.py`文件，运行即可。环境配置参考yolov3官方配置（https://github.com/ultralytics/yolov3）

博客地址： [Pyqt搭建YOLOV3目标检测界面（超详细+源代码）](https://blog.csdn.net/weixin_42035347/article/details/117064866)

#  以下为yolov3官方readme

# <a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank">
<img width="800" src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg"></a>
&nbsp

This repository represents Ultralytics open-source research into future object detection methods, and incorporates lessons learned and best practices evolved over thousands of hours of training and evolution on anonymized client datasets. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk.

<img width="800" src="https://user-images.githubusercontent.com/26833433/114424655-a0dc1e00-9bb8-11eb-9a2e-cbe21803f05c.png">
<details>
 <summary>YOLOv5-P5 640 Figure (click to expand)</summary>

<img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png">
</details>
<details>
 <summary>Figure Notes (click to expand)</summary>

* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. 
 * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
 * **Reproduce** by `python test.py --task study --data coco.yaml --iou 0.7 --weights yolov3.pt yolov3-spp.pt yolov3-tiny.pt yolov5l.pt`
</details>

## Branch Notice

The [ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository is now divided into two branches:
* [Master branch](https://github.com/ultralytics/yolov3/tree/master): Forward-compatible with all [YOLOv5](https://github.com/ultralytics/yolov5) models and methods (**recommended** ✅).
```bash
$ git clone https://github.com/ultralytics/yolov3  # master branch (default)
```
* [Archive branch](https://github.com/ultralytics/yolov3/tree/archive): Backwards-compatible with original [darknet](https://pjreddie.com/darknet/) *.cfg models (**no longer maintained** ⚠️). 
```bash
$ git clone https://github.com/ultralytics/yolov3 -b archive  # archive branch
```

## Pretrained Checkpoints

[assets3]: https://github.com/ultralytics/yolov3/releases
[assets5]: https://github.com/ultralytics/yolov5/releases

Model |size (pixels) |mAPval 0.5:0.95 |mAPtest 0.5:0.95 |mAPval 0.5 |Speed V100 (ms) | |params (M) |FLOPS 640 (B)
--- |--- |--- |--- |--- |--- |---|--- |---
[YOLOv3-tiny][assets3] |640 |17.6 |17.6 |34.8 |**1.2** | |8.8 |13.2
[YOLOv3][assets3] |640 |43.3 |43.3 |63.0 |4.1 | |61.9 |156.3
[YOLOv3-SPP][assets3] |640 |44.3 |44.3 |64.6 |4.1 | |63.0 |157.1
| | | | | | || |
[YOLOv5l][assets5] |640 |**48.2** |**48.2** |**66.9** |3.7 | |47.0 |115.4

<details>
 <summary>Table Notes (click to expand)</summary>

* APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. 
 * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` 
 * SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` 
 * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). 
</details>

## Requirements

Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
```bash
$ pip install -r requirements.txt
```

## Tutorials

* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️ RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
* [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518)&nbsp; 🌟 NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ 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)&nbsp; ⭐ NEW
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)

## Environments

YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):

- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov3"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov3"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt="Docker Pulls"></a>

## Inference

`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases) and saving results to `runs/detect`.
```bash
$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
```

To run inference on example images in `data/images`:
```bash
$ python detect.py --source data/images --weights yolov3.pt --conf 0.25
```
<img width="500" src="https://user-images.githubusercontent.com/26833433/100375993-06b37900-300f-11eb-8d2d-5fc7b22fbfbd.jpg">

### PyTorch Hub

To run **batched inference** with YOLOv3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36):
```python
import torch

# Model
model = torch.hub.load('ultralytics/yolov3', 'yolov3')  # or 'yolov3_spp', 'yolov3_tiny'

# Image
img = 'https://ultralytics.com/images/zidane.jpg'

# Inference
results = model(img)
results.print()  # or .show(), .save()
```

## Training

Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 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 yolov3.yaml --weights '' --batch-size 24
 yolov3-spp.yaml 24
 yolov3-tiny.yaml 64
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/100378028-af170c80-3012-11eb-8521-f0d2a8d021bc.png">

## Citation

[![DOI](https://zenodo.org/badge/146165888.svg)](https://zenodo.org/badge/latestdoi/146165888)

## About Us

Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.**
- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.**
- **Custom data training**, hyperparameter evolution, and model exportation to any destination.

For business inquiries and professional support requests please visit us at https://ultralytics.com.

## Contact

**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

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