keremberke commited on
Commit
b3c26f2
1 Parent(s): 95512a3

add ultralytics model card

Browse files
Files changed (1) hide show
  1. README.md +79 -0
README.md ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ---
3
+ tags:
4
+ - ultralyticsplus
5
+ - yolov8
6
+ - ultralytics
7
+ - yolo
8
+ - vision
9
+ - object-detection
10
+ - pytorch
11
+ - awesome-yolov8-models
12
+ library_name: ultralytics
13
+ library_version: 8.0.21
14
+ inference: false
15
+
16
+ datasets:
17
+ - keremberke/table-extraction
18
+
19
+ model-index:
20
+ - name: keremberke/yolov8m-table-extraction
21
+ results:
22
+ - task:
23
+ type: object-detection
24
+
25
+ dataset:
26
+ type: keremberke/table-extraction
27
+ name: table-extraction
28
+ split: validation
29
+
30
+ metrics:
31
+ - type: precision # since [email protected] is not available on hf.co/metrics
32
+ value: 0.95194 # min: 0.0 - max: 1.0
33
+ name: [email protected](box)
34
+ ---
35
+
36
+ <div align="center">
37
+ <img width="640" alt="keremberke/yolov8m-table-extraction" src="https://huggingface.co/keremberke/yolov8m-table-extraction/resolve/main/thumbnail.jpg">
38
+ </div>
39
+
40
+ ### Supported Labels
41
+
42
+ ```
43
+ ['bordered', 'borderless']
44
+ ```
45
+
46
+ ### How to use
47
+
48
+ - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
49
+
50
+ ```bash
51
+ pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
52
+ ```
53
+
54
+ - Load model and perform prediction:
55
+
56
+ ```python
57
+ from ultralyticsplus import YOLO, render_result
58
+
59
+ # load model
60
+ model = YOLO('keremberke/yolov8m-table-extraction')
61
+
62
+ # set model parameters
63
+ model.overrides['conf'] = 0.25 # NMS confidence threshold
64
+ model.overrides['iou'] = 0.45 # NMS IoU threshold
65
+ model.overrides['agnostic_nms'] = False # NMS class-agnostic
66
+ model.overrides['max_det'] = 1000 # maximum number of detections per image
67
+
68
+ # set image
69
+ image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
70
+
71
+ # perform inference
72
+ results = model.predict(image)
73
+
74
+ # observe results
75
+ print(results[0].boxes)
76
+ render = render_result(model=model, image=image, result=results[0])
77
+ render.show()
78
+ ```
79
+