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cfgs_res50_dota2.0_bcd_v6.py
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cfgs_res50_dota2.0_bcd_v6.py
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# -*- 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(','))
LR = 1e-3
SAVE_WEIGHTS_INTE = 40000
DECAY_STEP = np.array(DECAY_EPOCH, np.int32) * SAVE_WEIGHTS_INTE
MAX_ITERATION = SAVE_WEIGHTS_INTE * MAX_EPOCH
WARM_EPOCH = 1. / 8.
WARM_SETP = int(WARM_EPOCH * SAVE_WEIGHTS_INTE)
# dataset
DATASET_NAME = 'DOTA2.0'
CLASS_NUM = 18
# 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')
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 2.0
BCD_TAU = 2.0
BCD_FUNC = 0 # 0: sqrt 1: log
VERSION = 'RetinaNet_DOTA2.0_BCD_2x_20210723'
"""
RetinaNet-H + 1-1/(sqrt(bcd)+2)
FLOPs: 487525261; Trainable params: 33148131
This is your evaluation result for task 1:
mAP: 0.4748251390678757
ap of each class:
plane:0.7694004758275651,
baseball-diamond:0.4633958753542787,
bridge:0.39140982919175676,
ground-track-field:0.5911176240553634,
small-vehicle:0.41513007763044857,
large-vehicle:0.4719015966001979,
ship:0.5700450139195067,
tennis-court:0.7799866191766394,
basketball-court:0.567746193623308,
storage-tank:0.5067637510814136,
soccer-ball-field:0.36000188921274584,
roundabout:0.5053053207383681,
harbor:0.4518474589586364,
swimming-pool:0.5247063138022362,
helicopter:0.49441694419726545,
container-crane:0.12217758007758465,
airport:0.45779563157266656,
helipad:0.10370430820178039
The submitted information is :
Description: RetinaNet_DOTA2.0_BCD_2x_20210723_52w
Username: sjtu-deter
Institute: SJTU
Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
TeamMembers: yangxue
"""