# yolov8s

**Repository Path**: hf-models/yolov8s

## Basic Information

- **Project Name**: yolov8s
- **Description**: 一种先进的物体检测算法，以其出色的性能和高效率在图像识别领域受到关注。该模型在处理图像分类、物体检测和实例分割任务上表现优异，适合在多种硬件平台上运行。
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-25
- **Last Updated**: 2024-06-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

 
---
tags:
- ultralyticsplus
- ultralytics
- yolov8
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.4
inference: false

model-index:
- name: ultralyticsplus/yolov8s
  results:
  - task:
      type: object-detection

    metrics:
      - type: precision  # since mAP is not available on hf.co/metrics
        value: 0.449  # min: 0.0 - max: 1.0
        name: mAP
---

### Supported Labels

```
['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
```


### How to use

- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):

```bash
pip install -U ultralyticsplus==0.0.14
```

- Load model and perform prediction:

```python
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('ultralyticsplus/yolov8s')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```