You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
74 lines
3.9 KiB
74 lines
3.9 KiB
--- |
|
comments: true |
|
description: Kickstart your journey with YOLOv5. Learn how to install, run inference, and train models on your own images. Dive headfirst into object detection with PyTorch. |
|
keywords: YOLOv5, Quickstart, Installation, Inference, Training, Object detection, PyTorch, Ultralytics |
|
--- |
|
|
|
# YOLOv5 Quickstart |
|
|
|
See below for quickstart examples. |
|
|
|
## Install |
|
|
|
Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a |
|
[**Python>=3.8.0**](https://www.python.org/) environment, including |
|
[**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). |
|
|
|
```bash |
|
git clone https://github.com/ultralytics/yolov5 # clone |
|
cd yolov5 |
|
pip install -r requirements.txt # install |
|
``` |
|
|
|
## Inference |
|
|
|
YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). |
|
|
|
```python |
|
import torch |
|
|
|
# Model |
|
model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, 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](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. |
|
|
|
```bash |
|
python detect.py --weights yolov5s.pt --source 0 # webcam |
|
img.jpg # image |
|
vid.mp4 # video |
|
screen # screenshot |
|
path/ # directory |
|
list.txt # list of images |
|
list.streams # list of streams |
|
'path/*.jpg' # glob |
|
'https://youtu.be/LNwODJXcvt4' # YouTube |
|
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream |
|
``` |
|
|
|
## Training |
|
|
|
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) |
|
results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) |
|
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. |
|
|
|
```bash |
|
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 |
|
yolov5s 64 |
|
yolov5m 40 |
|
yolov5l 24 |
|
yolov5x 16 |
|
``` |
|
|
|
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" alt="YOLO training curves">
|
|
|