Update README.md

README.md

@@ -12,62 +12,20 @@ library_name: ultralytics
 library_version: 8.0.21
 inference: false
 
-datasets:
-- keremberke/table-extraction
+# ilsung-yolov8
+<h2>경북대학교 인공지능 혁신융합대학 사업단 산학 프로젝트</h2>
+<h3>CCTV현장 안전 인공지능 시스템 - (주)일성하이스코와 진행</h3>
 
-model-index:
-- name: keremberke/yolov8m-table-extraction
-  results:
-  - task:
-      type: object-detection
+Datasets: (주)일성하이스코의 IP카메라 2대로부터 원천 데이터 수집. 메타 데이터 annotation 작업을 위해 cvat.ai 사용<br>
+● class: {helmet_person, non_helmet_person}<br>
+● total image: 4052, train : val = 9 : 1
 
-    dataset:
-      type: keremberke/table-extraction
-      name: table-extraction
-      split: validation
+<img src="https://github.com/jk9169/ilsung-yolov8/blob/main/runs/detect/train5/labels.jpg?raw=true" width="400" height="400">
 
-    metrics:
-      - type: precision  # since mAP@0.5 is not available on hf.co/metrics
-        value: 0.95194  # min: 0.0 - max: 1.0
-        name: mAP@0.5(box)
----
-<div align="center">
-  <img width="640" alt="keremberke/yolov8m-table-extraction" src="https://huggingface.co/keremberke/yolov8m-table-extraction/resolve/main/thumbnail.jpg">
-</div>
-
-### Supported Labels
-
-```
-['bordered', 'borderless']
-```
-
-### How to use
-
-- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
-
-```bash
-pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
-```
-
-- Load model and perform prediction:
+Model: YOLOv8 </a><br>
+● Yolov8n<br>
+● Yolov8s
 
-```python
-from ultralyticsplus import YOLO, render_result
-# load model
-model = YOLO('keremberke/yolov8m-table-extraction')
-# 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()
-```
+<img src="https://github.com/jk9169/ilsung-yolov8/blob/main/runs/detect/train5/val_batch0_pred.jpg?raw=true">
 
 **More models available at: [awesome-yolov8-models](https://yolov8.xyz)**