ultralytics yolov8 object-detection pytorch license:mit region:us
---
license: mit
library_name: ultralytics
tags:
- yolov8
- object-detection
- pytorch
---

# TabDetect

### Supported Labels

```
['full_lined', 'not_full_lined']
```

### How to use

- Install ultralytics:

```bash
pip install -U ultralytics==8.0.227
```

- Load model and perform prediction:

```python
from ultralytics import YOLO

# load model
model = YOLO('camiloa2m/TabDetect-YOLOv8s')

# set model parameters (optional)
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 = '<URL or Path to an image'

# perform inference
results = model.predict(image)
```

### Dataset

[TNCR_Dataset](https://github.com/abdoelsayed2016/TNCR_Dataset). I merged some classes: class 0 (full_lined, merged_cells), class 1 (no_lines, partial_lined, partial_lined_merged_cells).

### Model summary (fused)

| Class          | Images | Instances | P     | R     | mAP50 | mAP50-95 |
|----------------|--------|-----------|-------|-------|-------|----------|
| all            | 1313   | 1906      | 0.957 | 0.926 | 0.973 | 0.938    |
| full_lined     | 1313   | 984       | 0.96  | 0.949 | 0.98  | 0.968    |
| not_full_lined | 1313   | 922       | 0.953 | 0.904 | 0.966 | 0.908    |


<div align="center">
  <img width="640" alt="camiloa2m/TabDetect-YOLOv8n" src="https://huggingface.co/camiloa2m/TabDetect-YOLOv8n/resolve/main/val_batch1_pred.jpg">
</div>

