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--- |
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license: gpl-3.0 |
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inference: true |
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tags: |
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- instance-segmentation |
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- computer-vision |
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- vision |
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- yolo |
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- yolov8 |
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datasets: |
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- detection-datasets/coco |
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pipeline_tag: image-segmentation |
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--- |
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### How to use |
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- Install yolov8: |
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```bash |
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pip install -U yolov8 |
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``` |
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- Load model and perform prediction: |
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```python |
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import yolov5 |
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# load model |
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model = yolov5.load('fcakyon/yolov5n-v7.0') |
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# set model parameters |
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model.conf = 0.25 # NMS confidence threshold |
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model.iou = 0.45 # NMS IoU threshold |
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model.agnostic = False # NMS class-agnostic |
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model.multi_label = False # NMS multiple labels per box |
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model.max_det = 1000 # maximum number of detections per image |
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# set image |
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img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' |
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# perform inference |
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results = model(img) |
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# inference with larger input size |
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results = model(img, size=640) |
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# inference with test time augmentation |
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results = model(img, augment=True) |
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# parse results |
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predictions = results.pred[0] |
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boxes = predictions[:, :4] # x1, y1, x2, y2 |
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scores = predictions[:, 4] |
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categories = predictions[:, 5] |
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# show detection bounding boxes on image |
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results.show() |
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# save results into "results/" folder |
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results.save(save_dir='results/') |
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``` |
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- Finetune the model on your custom dataset: |
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```bash |
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yolov5 train --img 640 --batch 16 --weights fcakyon/yolov5n-v7.0 --epochs 10 --device cuda:0 |
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``` |
