<div align="center">
# YOLOv8 Streamlit APP
<p>
<a align="center" href="https://ultralytics.com/yolov8" target="_blank">
<img width="50%" src="pic_bed/banner-yolov8.png"></a>
</p>
<br>
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/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/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
<br>
</div>
## Introduction
This repository supply a user-friendly interactive interface for [YOLOv8](https://github.com/ultralytics/ultralytics) and the interface is powered by [Streamlit](https://github.com/streamlit/streamlit). It could serve as a resource for future reference while working on your own projects.
## Features
- Feature1: Object detection task, segment task, pose task.
- Feature2:
Multiple detection models. `yolov8n`, `yolov8s`, `yolov8m`, `yolov8l`, `yolov8x`
Multiple segment models. `yolov8n-seg`, `yolov8s-seg`, `yolov8m-seg`, `yolov8l-seg`, `yolov8x-seg`
Multiple pose models. `yolov8n-pose`, `yolov8s-pose`, `yolov8m-pose`, `yolov8l-pose`, `yolov8x-pose`
- Feature3: Multiple input formats. `Image`, `Video`, `Webcam`
## Interactive Interface
### Image Input Interface
![image_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/image_input_detect_demo.png)
![image_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/image_input_pose_demo.png)
![image_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/image_input_segment_demo.png)
### Video Input Interface
![video_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/video_input_detect_demo.png)
![video_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/video_input_pose_demo.png)
![video_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/video_input_segment_demo.png)
### Webcam Input Interface
![webcam_input_demo](https://github.com/chenanga/YOLOv8-streamlit-app/blob/master/pic_bed/webcam_input_demo.png)
## Installation
### Create a new conda environment
```commandline
# create
conda create -n yolov8-streamlit python=3.8 -y
# activate
conda activate yolov8-streamlit
```
### Clone repository
```commandline
git clone https://github.com/chenanga/YOLOv8-streamlit-app
```
### Install packages
```commandline
# yolov8 dependencies
pip install ultralytics
# Streamlit dependencies
pip install streamlit
```
### Download Pre-trained YOLOv8 Detection Weights
Create a directory named `weights` and create a subdirectory named `detection` and save the downloaded YOLOv8 object detection weights inside this directory. The weight files can be downloaded from the table below.
---
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt)
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基于streamlit的YOLOv8可视化交互界面
共180个文件
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png:8个
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基于streamlit的YOLOv8可视化交互界面 (180个子文件)
.gitignore 59B
bus.jpg 134KB
zidane.jpg 49KB
README.md 10KB
README.md 2KB
README.md 2KB
webcam_input_demo.png 2.18MB
video_input_detect_demo.png 864KB
image_input_pose_demo.png 642KB
image_input_detect_demo.png 634KB
image_input_segment_demo.png 621KB
video_input_pose_demo.png 612KB
video_input_segment_demo.png 562KB
banner-yolov8.png 249KB
v5loader.py 50KB
exporter.py 43KB
metrics.py 41KB
augment.py 36KB
tasks.py 33KB
trainer.py 31KB
ops.py 28KB
__init__.py 28KB
autobackend.py 25KB
plotting.py 24KB
results.py 24KB
utils.py 23KB
encoders.py 22KB
torch_utils.py 22KB
model.py 19KB
loss.py 19KB
kalman_filter.py 18KB
__init__.py 18KB
checks.py 17KB
v5augmentations.py 17KB
prompt.py 16KB
predictor.py 16KB
stream_loaders.py 16KB
transformer.py 16KB
benchmarks.py 15KB
head.py 15KB
mask_generator.py 15KB
val.py 14KB
instance.py 14KB
byte_tracker.py 14KB
tal.py 13KB
dataset.py 13KB
amg.py 13KB
loss.py 13KB
comet.py 13KB
ops.py 13KB
val.py 13KB
downloads.py 12KB
base.py 12KB
autoshape.py 12KB
val.py 12KB
gmc.py 12KB
block.py 12KB
validator.py 11KB
conv.py 11KB
prompt_predictor.py 11KB
val.py 11KB
utils.py 9KB
converter.py 9KB
matching.py 9KB
transformer.py 8KB
session.py 8KB
model.py 7KB
sam.py 7KB
train.py 7KB
train.py 7KB
build.py 6KB
val.py 6KB
decoders.py 6KB
clearml.py 6KB
bot_sort.py 6KB
base.py 5KB
tuner.py 5KB
utils.py 5KB
model.py 5KB
auth.py 5KB
val.py 5KB
model.py 4KB
__init__.py 4KB
dvc.py 4KB
autosize.py 4KB
autobatch.py 4KB
build.py 4KB
neptune.py 4KB
files.py 4KB
hub.py 3KB
utils.py 3KB
train.py 3KB
predict.py 3KB
predict.py 3KB
train.py 3KB
mlflow.py 3KB
dist.py 3KB
app.py 2KB
train.py 2KB
predict.py 2KB
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