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chanelcolgate
/
chamdiemgianhang-vsk-v6

Object Detection
TensorBoard
PyTorch
ultralytics
v8
ultralyticsplus
yolov8
yolo
vision
Eval Results
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chamdiemgianhang-vsk-v6
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---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch

library_name: ultralytics
library_version: 8.0.239
inference: false

datasets:
- chanelcolgate/yenthienviet

model-index:
- name: chanelcolgate/chamdiemgianhang-vsk-v6
  results:
  - task:
      type: object-detection

    dataset:
      type: chanelcolgate/yenthienviet
      name: yenthienviet
      split: validation

    metrics:
      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
        value: 0.99419  # min: 0.0 - max: 1.0
        name: mAP@0.5(box)
---

<div align="center">
  <img width="640" alt="chanelcolgate/chamdiemgianhang-vsk-v6" src="https://huggingface.co/chanelcolgate/chamdiemgianhang-vsk-v6/resolve/main/thumbnail.jpg">
</div>

### Supported Labels

```
['BOM_GEN', 'BOM_JUN', 'BOM_KID', 'BOM_SAC', 'BOM_THV', 'BOM_THX', 'BOM_VTG', 'BOM_YTV', 'HOP_FEJ', 'HOP_FRE', 'HOP_JUN', 'HOP_POC', 'HOP_VTG', 'HOP_YTV', 'LOC_JUN', 'LOC_KID', 'LOC_YTV', 'LOO_DAU', 'LOO_KID', 'LOO_MAM', 'LOO_YTV', 'POS_GLI', 'POS_LON', 'POS_NHO', 'POS_THA', 'TUI_GEN', 'TUI_JUN', 'TUI_KID', 'TUI_SAC', 'TUI_THV', 'TUI_THX', 'TUI_VTG', 'TUI_YTV']
```

### How to use

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

```bash
pip install ultralyticsplus==0.1.0 ultralytics==8.0.239
```

- Load model and perform prediction:

```python
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('chanelcolgate/chamdiemgianhang-vsk-v6')

# 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()
```