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cfgs_res50_dota1.5_bcd_v6.py
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cfgs_res50_dota1.5_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 = 32000
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 = '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')
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 2.0
BCD_TAU = 2.0
BCD_FUNC = 0 # 0: sqrt 1: log
VERSION = 'RetinaNet_DOTA1.5_BCD_2x_20210721'
"""
RetinaNet-H + 1-1/(sqrt(bcd)+2)
FLOPs: 485782931; Trainable params: 33051321
This is your evaluation result for task 1:
mAP: 0.6078214458191175
ap of each class:
plane:0.7960611799772355,
baseball-diamond:0.7361295784085993,
bridge:0.4098600873125639,
ground-track-field:0.6723916268287933,
small-vehicle:0.4969461358511616,
large-vehicle:0.6750486907026878,
ship:0.779737113621211,
tennis-court:0.8969323316457076,
basketball-court:0.7407904680051254,
storage-tank:0.6237031460031418,
soccer-ball-field:0.4636397916269505,
roundabout:0.6386039475755304,
harbor:0.6334257658232346,
swimming-pool:0.6180235560899322,
helicopter:0.5246413185159465,
container-crane:0.01920839511805635
The submitted information is :
Description: RetinaNet_DOTA1.5_BCD_2x_20210721_41.6w
Username: SJTU-Det
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""