Update README.md

README.md

@@ -1,3 +1,62 @@
 ---
-license: apache-2.0
+license: mit
+library_name: ultralytics
+tags:
+- yolov8
+- object-detection
+- pytorch
 ---
+
+# TabDetect
+
+<div align="center">
+  <img width="640" alt="https://huggingface.co/camiloa2m/TabDetect-YOLOv8s/blob/main/val_batch1_pred.jpg>
+</div>
+
+### 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
+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)
+```
+
+### 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    |
+