Yolo Object Name Mapping

After executing yolo related examples, there will be a class number shown in every bounding boxes.

C example

sudo ./kl520_demo_generic_image_inference_post_yolo

connect device ... OK
upload firmware ... OK
upload model ... OK
read image ... OK

starting inference loop 100 times:
....................................................................................................

inference loop is done, starting post-processing ...

doing tiny yolo v3 post-processing ...

detectable class count : 80
box count : 6
Box 0 (x1, y1, x2, y2, score, class) = 45.0, 57.0, 93.0, 196.0, 0.965018, 0
Box 1 (x1, y1, x2, y2, score, class) = 43.0, 95.0, 100.0, 211.0, 0.465116, 1
Box 2 (x1, y1, x2, y2, score, class) = 122.0, 68.0, 218.0, 185.0, 0.997959, 2
Box 3 (x1, y1, x2, y2, score, class) = 87.0, 84.0, 131.0, 118.0, 0.499075, 2
Box 4 (x1, y1, x2, y2, score, class) = 28.0, 77.0, 55.0, 100.0, 0.367952, 2
Box 5 (x1, y1, x2, y2, score, class) = 1.0, 84.0, 50.0, 181.0, 0.229727, 2

output bounding boxes on 'output_bike_cars_street_224x224.bmp'

Python example

$ python3 KL520DemoGenericInferencePostYolo.py

[Connect Device]
 - Success
[Set Device Timeout]
 - Success
[Upload Firmware]
 - Success
[Upload Model]
 - Success
[Read Image]
 - Success
[Starting Inference Work]
 - Starting inference loop 50 times
 - ..................................................
[Retrieve Inference Node Output ]
 - Success
[Tiny Yolo V3 Post-Processing]
 - Success
[Result]
{
    "class_count": 80,
    "box_count": 6,
    "box_list": {
        "0": {
            "x1": 46,
            "y1": 62,
            "x2": 91,
            "y2": 191,
            "score": 0.965,
            "class_num": 0
        },
        "1": {
            "x1": 44,
            "y1": 96,
            "x2": 99,
            "y2": 209,
            "score": 0.4651,
            "class_num": 1
        },
        "2": {
            "x1": 122,
            "y1": 70,
            "x2": 218,
            "y2": 183,
            "score": 0.998,
            "class_num": 2
        },
        "3": {
            "x1": 87,
            "y1": 85,
            "x2": 131,
            "y2": 117,
            "score": 0.4991,
            "class_num": 2
        },
        "4": {
            "x1": 28,
            "y1": 77,
            "x2": 55,
            "y2": 100,
            "score": 0.368,
            "class_num": 2
        },
        "5": {
            "x1": 3,
            "y1": 84,
            "x2": 48,
            "y2": 181,
            "score": 0.2297,
            "class_num": 2
        }
    }
}
[Output Result Image]
 - Output bounding boxes on 'output_bike_cars_street_224x224.bmp'

The table listed below provides the corresponding object name for each class number.

Class Number	Object Name
0	Person
1	Bicycle
2	Car
3	Motorbike
4	Aeroplane
5	Bus
6	Train
7	Truck
8	Boat
9	Traffic Light
10	Fire Hydrant
11	Stop Sign
12	Parking Meter
13	Bench
14	Bird
15	Cat
16	Dog
17	Horse
18	Sheep
19	Cow
20	Elephant
21	Bear
22	Zebra
23	giraffe
24	Backpack
25	Umbrella
26	Handbag
27	Tie
28	Suitcase
29	Frisbee
30	Skis
31	Snowboard
32	Sports Ball
33	Kite
34	Baseball Bat
35	Baseball Glove
36	Skateboard
37	Surfboard
38	Tennis Racket
39	Bottle
40	Wine Glass
41	Cup
42	Fork
43	Knife
44	Spoon
45	Bowl
46	Banana
47	Apple
48	Sandwich
49	Orange
50	Broccoli
51	Carrot
52	Hot Dog
53	Pizza
54	Donut
55	Cake
56	Chair
57	Sofa
58	Potted Plant
59	Bed
60	Dining Table
61	Toilet
62	Tv Monitor
63	Laptop
64	Mouse
65	Remote
66	Keyboard
67	Cell Phone
68	Microwave
69	Oven
70	Toaster
71	Sink
72	Refrigerator
73	Book
74	Clock
75	Vase
76	Scissors
77	Teddy Bear
78	Hair Drier
79	Toothbrush