TabDetect

Supported Labels

['full_lined', 'not_full_lined']

How to use

Install ultralytics:

pip install -U ultralytics==8.0.227

Load model and perform prediction:

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. 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