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cfgs_res50_dota_v1.py
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cfgs_res50_dota_v1.py
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# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from configs._base_.models.faster_rcnn_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 = "0,1,2"
NUM_GPU = len(GPU_GROUP.strip().split(','))
LR = 0.001 * 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')
VERSION = 'FPN_Res50D_DOTA_1x_20201031'
"""
R2CNN
FLOPs: 1024153266; Trainable params: 41772682
This is your result for task 1:
mAP: 0.7227414456963894
ap of each class:
plane:0.8954291131230108,
baseball-diamond:0.7615013248230833,
bridge:0.47589589239010427,
ground-track-field:0.6484503831218632,
small-vehicle:0.7616171143029637,
large-vehicle:0.7395101403930869,
ship:0.8587426481796258,
tennis-court:0.9022025499507798,
basketball-court:0.8327346869026073,
storage-tank:0.8431585743608815,
soccer-ball-field:0.5106006620292729,
roundabout:0.6561468034665185,
harbor:0.6530002955426998,
swimming-pool:0.6823392612570894,
helicopter:0.6197922356022552
The submitted information is :
Description: FPN_Res50D_DOTA_1x_20201031_37.8w
Username: SJTU-Det
Institute: SJTU
Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
TeamMembers: yangxue
"""