forked from yangxue0827/RotationDetection
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcfgs.py
97 lines (78 loc) · 2.59 KB
/
cfgs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from alpharotate.utils.pretrain_zoo import PretrainModelZoo
from configs._base_.models.retinanet_r50_fpn import *
from configs._base_.datasets.dota_detection import *
from configs._base_.schedules.schedule_1x import *
# schedule
BATCH_SIZE = 1
GPU_GROUP = "0"
NUM_GPU = len(GPU_GROUP.strip().split(','))
SAVE_WEIGHTS_INTE = 27000
DECAY_STEP = np.array(DECAY_EPOCH, np.int32) * SAVE_WEIGHTS_INTE
MAX_ITERATION = SAVE_WEIGHTS_INTE * MAX_EPOCH
WARM_SETP = int(WARM_EPOCH * SAVE_WEIGHTS_INTE)
# model
# backbone
pretrain_zoo = PretrainModelZoo()
PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 0.01
VERSION = 'RetinaNet_DOTA_KF_1x_20210902'
"""
RetinaNet-H + kfiou (exp(1-IoU)-1)
loss = (loss_1.reshape([n, 1]) + loss_2).reshape([n*n,1])
loss = sum(loss)
loss /= n
This is your result for task 1:
mAP: 0.6992391773413088
ap of each class:
plane:0.892002960041615,
baseball-diamond:0.7732858199679905,
bridge:0.461488701631356,
ground-track-field:0.6568226387825112,
small-vehicle:0.7191446514672439,
large-vehicle:0.6496180915914522,
ship:0.7756721605851568,
tennis-court:0.8957877374986684,
basketball-court:0.8218284672164252,
storage-tank:0.7863219622742004,
soccer-ball-field:0.5704904341914929,
roundabout:0.6612983503314489,
harbor:0.6307078540759418,
swimming-pool:0.6814249757638241,
helicopter:0.5126928547003046
The submitted information is :
Description: RetinaNet_DOTA_KF_1x_20210902_35.1w
Username: yangxue
Institute: DetectionTeamUCAS
Emailadress: [email protected]
TeamMembers: yangxue, yangjirui
This is your result for task 1:
mAP: 0.7064133708777482
ap of each class:
plane:0.8949501034578764,
baseball-diamond:0.7796459013723996,
bridge:0.46303070580719785,
ground-track-field:0.6726390727983068,
small-vehicle:0.7256290420401867,
large-vehicle:0.6561151622340775,
ship:0.7815922299808545,
tennis-court:0.9077429747421872,
basketball-court:0.8301767177490947,
storage-tank:0.7914564206644479,
soccer-ball-field:0.5844254572326262,
roundabout:0.6633575297546964,
harbor:0.6368083141217261,
swimming-pool:0.6855568710558761,
helicopter:0.52307406015467
The submitted information is :
Description: RetinaNet_DOTA_KF_1x_20210902_35.1w_cpunms
Username: SJTU-Det
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""