Agar natija olib kursak
cls: tensor([0., 0., 0., 0., 0., 0., 0.])
conf: tensor([0.8909, 0.8682, 0.8674, 0.8622, 0.8439, 0.8392, 0.7159])
data: tensor([[1.6254e+02, 2.2389e+01, 2.5266e+02, 1.6701e+02, 8.9091e-01, 0.0000e+00],
[2.3503e+02, 3.1486e+01, 2.9971e+02, 1.6686e+02, 8.6820e-01, 0.0000e+00],
[2.1997e+01, 5.3749e+01, 7.4538e+01, 1.6752e+02, 8.6741e-01, 0.0000e+00],
[1.1284e+02, 3.2849e+01, 1.6784e+02, 1.6768e+02, 8.6221e-01, 0.0000e+00],
[6.3885e+01, 4.3812e+01, 1.1679e+02, 1.6726e+02, 8.4389e-01, 0.0000e+00],
[3.2759e-02, 5.2869e+00, 4.6813e+01, 1.6724e+02, 8.3916e-01, 0.0000e+00],
[1.5617e+02, 1.0563e+01, 1.9749e+02, 9.0878e+01, 7.1594e-01, 0.0000e+00]])
id: None
is_track: False
orig_shape: (168, 300)
shape: torch.Size([7, 6])
xywh: tensor([[207.5979, 94.6973, 90.1146, 144.6161],
[267.3671, 99.1744, 64.6808, 135.3771],
[ 48.2676, 110.6329, 52.5402, 113.7673],
[140.3382, 100.2657, 55.0010, 134.8336],
[ 90.3380, 105.5342, 52.9057, 123.4454],
[ 23.4231, 86.2625, 46.7806, 161.9512],
[176.8280, 50.7202, 41.3200, 80.3153]])
xywhn: tensor([[0.6920, 0.5637, 0.3004, 0.8608],
[0.8912, 0.5903, 0.2156, 0.8058],
[0.1609, 0.6585, 0.1751, 0.6772],
[0.4678, 0.5968, 0.1833, 0.8026],
[0.3011, 0.6282, 0.1764, 0.7348],
[0.0781, 0.5135, 0.1559, 0.9640],
[0.5894, 0.3019, 0.1377, 0.4781]])
xyxy: tensor([[1.6254e+02, 2.2389e+01, 2.5266e+02, 1.6701e+02],
[2.3503e+02, 3.1486e+01, 2.9971e+02, 1.6686e+02],
[2.1997e+01, 5.3749e+01, 7.4538e+01, 1.6752e+02],
[1.1284e+02, 3.2849e+01, 1.6784e+02, 1.6768e+02],
[6.3885e+01, 4.3812e+01, 1.1679e+02, 1.6726e+02],
[3.2759e-02, 5.2869e+00, 4.6813e+01, 1.6724e+02],
[1.5617e+02, 1.0563e+01, 1.9749e+02, 9.0878e+01]])
xyxyn: tensor([[5.4180e-01, 1.3327e-01, 8.4218e-01, 9.9408e-01],
[7.8342e-01, 1.8742e-01, 9.9902e-01, 9.9323e-01],
[7.3325e-02, 3.1994e-01, 2.4846e-01, 9.9712e-01],
[3.7613e-01, 1.9553e-01, 5.5946e-01, 9.9811e-01],
[2.1295e-01, 2.6078e-01, 3.8930e-01, 9.9558e-01],
[1.0920e-04, 3.1470e-02, 1.5604e-01, 9.9546e-01],
[5.2056e-01, 6.2872e-02, 6.5829e-01, 5.4094e-01]])
Shunga uxshagan natija beradi.
Natija siz ishlatayotgan freymworkga bogliq (Pytorch, ....)
Demak birinchi listda sinf (0-bu odam umumiy sinflar
names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 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: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 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'}
)
keyin esa conf yane har bitta obektni aniqlash aniqligi ruyxati (rasmda bir nechta obekt buladi)
keyin esa bizga obekt joylashuvi xywh yane yuqori chap burchag koordinatasi va w-uzunlik h-balandlik beriladi.
bundan tashqari biz turtburchak asosida malumotni olishimiz mumkin yuqori chao va pastgi ong xyxy
