# An object Detection method based on YOLOv5
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<p>
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<br>
<p>
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
</p>
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## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## <div align="center">Quick Start Examples</div>
<details open>
<summary>Install</summary>
[**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/):
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt
```
</details>
<details open>
<summary>Inference</summary>
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.
```
</details>
<details>
<summary>Inference with detect.py</summary>
`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.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
```
</details>
<details>
<summary>Training</summary>
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
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
1 硬件配置说明
1.1 计算平台
| ***\*GPU\**** | 128-core Maxwell |
| ---------------------- | ---------------------------------------------------------- |
| ***\*CPU\**** | Quad-core ARM [email protected] |
| ***\*Memory\**** | 4GB 64-bit LPDDR4 25.6GB/s |
| ***\*Storage\**** | microSD(not included) |
| ***\*Video Encode\**** | 4K@30\|4x 1080p @30\|9x 720p @30 (H.264/H.265) |
| ***\*Video Decode\**** | 4K@60\|2x 4K @30\|8x 1080p @30\|18x 720p @30 (H.264/H.265) |
| ***\*Camera\**** | 1x MIPI CSI-2 DPHY lanes |
| ***\*Connectivity\**** | Gigabit Ethernet, M.2 Key E |
| ***\*Display\**** | HDMI 2.0 and eDP 1.4 |
| ***\*USB\**** | 4x USB 3.0, USB 2.0 Micro-B |
| ***\*Others\**** | GPIO, I2C, I2S, SPI, UART |
| ***\*Mechanical\**** | 69mm×45mm, 260-pin edge connector |
1.2 摄像头
摄像头使用大赛组委会规定 Intel SR300,具体参数如下:
| ***\*基本要素\**** | |
| ----------------------------------------------------- | ----------------------- |
| ***\*产品集\**** | 英特尔® 实感™ 摄像头 |
| ***\*状态\**** | Launched |
| ***\*发行日期\**** | Q1'16 |
| ***\*深度技术\**** | Coded Light |
| ***\*操作规范\**** | |
| ***\*操作范围(最小\**** ***\*-\**** ***\*最大)\**** | 0.3m - 2m |
| ***\*深度分辨率和 FPS\**** | VGA 30fps |
| ***\*视野深度\**** | H: 73, V: 59, D: 90
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中国机器人大赛-先进视觉赛-3d目标检测.zip (109个子文件)
Dockerfile 2KB
Dockerfile 821B
.dockerignore 4KB
.gitattributes 75B
.gitignore 4KB
tutorial.ipynb 48KB
LICENSE 34KB
README.md 13KB
README.md 10KB
CONTRIBUTING.md 5KB
README.md 2KB
bug-report.md 1KB
feature-request.md 739B
question.md 139B
图片2.png 2.78MB
图片1.png 2.73MB
图片3.png 602KB
图片4.png 75KB
test.png 1B
datasets.py 43KB
general.py 33KB
train.py 31KB
wandb_utils.py 25KB
tf.py 20KB
common.py 20KB
plots.py 19KB
val.py 17KB
export.py 16KB
detect.py 16KB
yolo.py 14KB
torch_utils.py 14KB
metrics.py 13KB
augmentations.py 11KB
loss.py 9KB
autoanchor.py 7KB
__init__.py 6KB
hubconf.py 6KB
downloads.py 6KB
prepare_data.py 5KB
experimental.py 4KB
activations.py 4KB
callbacks.py 2KB
gen_wts.py 2KB
resume.py 1KB
restapi.py 1KB
sweep.py 989B
log_dataset.py 891B
example_request.py 299B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
__init__.py 0B
userdata.sh 1KB
get_coco.sh 900B
mime.sh 780B
get_coco128.sh 615B
download_weights.sh 443B
yolov5_train.txt 569KB
yolov5_val.txt 146KB
requirements.txt 892B
additional_requirements.txt 105B
ncdx_ncdx3dsb_R3.txt 62B
ncdx_ncdx3dsb_R4.txt 31B
Objects365.yaml 7KB
xView.yaml 5KB
anchors.yaml 3KB
VisDrone.yaml 3KB
Argoverse.yaml 3KB
sweep.yaml 2KB
SKU-110K.yaml 2KB
coco.yaml 2KB
yolov5-p7.yaml 2KB
GlobalWheat2020.yaml 2KB
yolov5x6.yaml 2KB
yolov5s6.yaml 2KB
yolov5n6.yaml 2KB
yolov5m6.yaml 2KB
yolov5l6.yaml 2KB
coco128.yaml 2KB
hyp.scratch-low.yaml 2KB
hyp.scratch-high.yaml 2KB
hyp.scratch.yaml 2KB
yolov5-p6.yaml 2KB
yolov5-p2.yaml 2KB
yolov3-spp.yaml 2KB
yolov3.yaml 2KB
yolov5-panet.yaml 1KB
yolov5s-ghost.yaml 1KB
yolov5s-transformer.yaml 1KB
yolov5-bifpn.yaml 1KB
yolov5m.yaml 1KB
yolov5s.yaml 1KB
yolov5x.yaml 1KB
yolov5n.yaml 1KB
yolov5l.yaml 1KB
yolov5-fpn.yaml 1KB
yolov3-tiny.yaml 1KB
hyp.finetune.yaml 907B
VOC.yaml 753B
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