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cfgs_res50_dota_fcos_v3.py
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cfgs_res50_dota_fcos_v3.py
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# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from configs._base_.models.retinanet_r50_fpn import *
from configs._base_.datasets.dota_detection import *
from configs._base_.schedules.schedule_1x import *
from alpharotate.utils.pretrain_zoo import PretrainModelZoo
# schedule
BATCH_SIZE = 1
GPU_GROUP = "0"
NUM_GPU = len(GPU_GROUP.strip().split(','))
LR = 1e-3 * BATCH_SIZE * NUM_GPU
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)
# dataset
# 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 = 1.0
CTR_WEIGHT = 1.0
REG_LOSS_MODE = 0
VERSION = 'FCOS_DOTA_RSDet_1x_20210622'
"""
FCOS + modulated loss
FLOPs: 468484120; Trainable params: 32090136
This is your result for task 1:
mAP: 0.6790645938261939
ap of each class:
plane:0.8729861339230366,
baseball-diamond:0.7538884620192762,
bridge:0.4587526572337119,
ground-track-field:0.5802029845851726,
small-vehicle:0.7337562383425493,
large-vehicle:0.634861376842494,
ship:0.7677495389605099,
tennis-court:0.8914462748625346,
basketball-court:0.7506157206033095,
storage-tank:0.8285886854886657,
soccer-ball-field:0.5257314976581912,
roundabout:0.6079512703389356,
harbor:0.6050630359585935,
swimming-pool:0.6476218493173386,
helicopter:0.5267531812585877
The submitted information is :
Description: FCOS_DOTA_RSDet_1x_20210622_35.1w
Username: SJTU-Det
Institute: SJTU
Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
TeamMembers: yangxue
"""