README.md

 
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-segmentation
- pytorch
library_name: ultralytics
library_version: 8.0.6
inference: false

model-index:
- name: fcakyon/test-model
  results:
  - task:
      type: image-segmentation

    metrics:
      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
        value: 0.63311  # min: 0.0 - max: 1.0
        name: mAP@0.5(box)
      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
        value: 0.60214  # min: 0.0 - max: 1.0
        name: mAP@0.5(mask)
---

<div align="center">
  <img width="640" alt="fcakyon/test-model" src="https://huggingface.co/fcakyon/test-model/resolve/main/thumbnail.jpg">
</div>

### Supported Labels

```
['Cracks-and-spalling', 'object']
```

### How to use

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

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

- Load model and perform prediction:

```python
from ultralyticsplus import YOLO, render_model_output

# load model
model = YOLO('fcakyon/test-model')

# 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
for result in model.predict(image, return_outputs=True):
    print(result["det"]) # [[x1, y1, x2, y2, conf, class]]
    print(result["segment"]) # [segmentation mask]
    render = render_model_output(model=model, image=image, model_output=result)
    render.show()
```