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---
license: gpl-3.0
inference: true
tags:
- instance-segmentation
- computer-vision
- vision
- yolo
- yolov8
datasets:
- detection-datasets/coco
pipeline_tag: image-segmentation
---
### How to use
- Install yolov8:
```bash
pip install -U yolov8
```
- Load model and perform prediction:
```python
import yolov5
# load model
model = yolov5.load('fcakyon/yolov5n-v7.0')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.agnostic = False # NMS class-agnostic
model.multi_label = False # NMS multiple labels per box
model.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
results = model(img)
# inference with larger input size
results = model(img, size=640)
# inference with test time augmentation
results = model(img, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
# save results into "results/" folder
results.save(save_dir='results/')
```
- Finetune the model on your custom dataset:
```bash
yolov5 train --img 640 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 10 --device cuda:0
``` |