Object Detection
YOLOv10
computer-vision
pypi
kadirnar commited on
Commit
5d73d36
1 Parent(s): e36e444

Initial commit for yolov10b

Browse files
Files changed (2) hide show
  1. README.md +78 -0
  2. yolov10b.pt +3 -0
README.md ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ---
3
+ license: agpl-3.0
4
+ tags:
5
+ - object-detection
6
+ - computer-vision
7
+ - yolov10
8
+ - pypi
9
+ datasets:
10
+ - detection-datasets/coco
11
+ ---
12
+
13
+ ### Model Description
14
+ [YOLOv10: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2405.14458v1)
15
+
16
+ [Paper Repo: Implementation of paper - YOLOv10](https://github.com/THU-MIG/yolov10)
17
+
18
+ ### Installation
19
+ ```
20
+ pip install supervision git+https://github.com/THU-MIG/yolov10.git
21
+ ```
22
+
23
+ ### Yolov10 Inference
24
+ ```python
25
+ from ultralytics import YOLOv10
26
+ import supervision as sv
27
+ import cv2
28
+
29
+ MODEL_PATH = 'yolov10n.pt'
30
+ IMAGE_PATH = 'dog.jpeg'
31
+
32
+ model = YOLOv10(MODEL_PATH)
33
+ image = cv2.imread(IMAGE_PATH)
34
+ results = model(source=image, conf=0.25, verbose=False)[0]
35
+ detections = sv.Detections.from_ultralytics(results)
36
+ box_annotator = sv.BoxAnnotator()
37
+
38
+ category_dict = {
39
+ 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
40
+ 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
41
+ 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
42
+ 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
43
+ 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
44
+ 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
45
+ 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
46
+ 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
47
+ 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
48
+ 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
49
+ 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
50
+ 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
51
+ 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
52
+ 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
53
+ 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
54
+ 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
55
+ }
56
+
57
+ labels = [
58
+ f"{category_dict[class_id]} {confidence:.2f}"
59
+ for class_id, confidence in zip(detections.class_id, detections.confidence)
60
+ ]
61
+ annotated_image = box_annotator.annotate(
62
+ image.copy(), detections=detections, labels=labels
63
+ )
64
+
65
+ cv2.imwrite('annotated_dog.jpeg', annotated_image)
66
+ ```
67
+
68
+ ### BibTeX Entry and Citation Info
69
+ ```
70
+ @misc{wang2024yolov10,
71
+ title={YOLOv10: Real-Time End-to-End Object Detection},
72
+ author={Ao Wang and Hui Chen and Lihao Liu and Kai Chen and Zijia Lin and Jungong Han and Guiguang Ding},
73
+ year={2024},
74
+ eprint={2405.14458},
75
+ archivePrefix={arXiv},
76
+ primaryClass={cs.CV}
77
+ }
78
+ ```
yolov10b.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3846434cbf0016b663a1ccd6d843c48468f6852f4feeddcb9f67f9182168c142
3
+ size 82979199