---
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 `ultralytics` and `ultralyticsplus`:

```bash
pip install -U ultralytics ultralyticsplus
```

- Load model and perform prediction:

```python
from ultralyticsplus import YOLO, render_predictions

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

# perform inference
for result in model.predict(img, imgsz=640, return_outputs=True):
    print(result) # [x1, y1, x2, y2, conf, class]
    render = render_predictions(model, img=img, det=result["det"])
    render.show()
```