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cfgs_res50_dota_r3det_v1.py
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cfgs_res50_dota_r3det_v1.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 # r3det only support 1
GPU_GROUP = '0,1,2'
NUM_GPU = len(GPU_GROUP.strip().split(','))
SAVE_WEIGHTS_INTE = 27000 * 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
# 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
USE_IOU_FACTOR = False
VERSION = 'RetinaNet_DOTA_R3Det_2x_20191108'
"""
This is your result for task 1:
mAP: 0.7066194189913816
ap of each class:
plane:0.8905480010393588,
baseball-diamond:0.7845764249543027,
bridge:0.4415489914209597,
ground-track-field:0.6515721505439082,
small-vehicle:0.7509226622459368,
large-vehicle:0.7288453788151275,
ship:0.8604046905135039,
tennis-court:0.9082569687774237,
basketball-court:0.8141347275878138,
storage-tank:0.8253027715641935,
soccer-ball-field:0.5623560181901192,
roundabout:0.6100656068973895,
harbor:0.5648618127447264,
swimming-pool:0.6767393616949172,
helicopter:0.5291557178810407
The submitted information is :
Description: RetinaNet_DOTA_R3Det_2x_20191108_70.2w
Username: SJTU-Det
Institute: SJTU
Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
TeamMembers: yangxue
"""