-
Notifications
You must be signed in to change notification settings - Fork 183
/
cfgs_res50_dota1.5_r3det_kl_v2.py
79 lines (63 loc) · 2.05 KB
/
cfgs_res50_dota1.5_r3det_kl_v2.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
# -*- 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 # r3det only support 1
GPU_GROUP = '0,1,2'
NUM_GPU = len(GPU_GROUP.strip().split(','))
LR = 1e-3
SAVE_WEIGHTS_INTE = 32000 * 2
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)
# dataset
DATASET_NAME = 'DOTA1.5'
CLASS_NUM = 16
# model
pretrain_zoo = PretrainModelZoo()
PRETRAINED_CKPT = pretrain_zoo.pretrain_weight_path(NET_NAME, ROOT_PATH)
TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights')
# bbox head
NUM_REFINE_STAGE = 1
# sample
REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 2.0
KL_TAU = 2.0
KL_FUNC = 1 # 0: sqrt 1: log
VERSION = 'RetinaNet_DOTA1.5_R3Det_KL_2x_20210423'
"""
r3det + kl + sqrt tau=2
FLOPs: 1033867194; Trainable params: 37820366
This is your evaluation result for task 1:
mAP: 0.6517834978722121
ap of each class:
plane:0.8029868768935169,
baseball-diamond:0.7274977717100799,
bridge:0.4737323854654457,
ground-track-field:0.6018401719491528,
small-vehicle:0.6324111068964658,
large-vehicle:0.7514635107769393,
ship:0.8607054711211418,
tennis-court:0.8952458550652271,
basketball-court:0.7352291137148709,
storage-tank:0.7292558330841545,
soccer-ball-field:0.5035864952531544,
roundabout:0.6623234599022446,
harbor:0.6457653133001389,
swimming-pool:0.6910883017090202,
helicopter:0.5783718622915984,
container-crane:0.13703243682224242
The submitted information is :
Description: RetinaNet_DOTA1.5_R3Det_KL_2x_20210423_108.8w
Username: SJTU-Det
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""