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---
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>
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