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cfgs_res50_dota_r3det_dcl_v1.py
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cfgs_res50_dota_r3det_dcl_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 = '0,1,2,3'
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
ANGLE_RANGE = 180
# sample
REFINE_IOU_POSITIVE_THRESHOLD = [0.6, 0.7]
REFINE_IOU_NEGATIVE_THRESHOLD = [0.5, 0.6]
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 1.0
ANGLE_WEIGHT = 0.5
USE_IOU_FACTOR = True
# DCL
OMEGA = 180 / 256.
ANGLE_MODE = 0 # {0: BCL, 1: GCL}
VERSION = 'RetinaNet_DOTA_R3Det_DCL_B_2x_20201024'
"""
FLOPs: 1263438187; Trainable params: 37785791
This is your result for task 1:
mAP: 0.7121184762618237
ap of each class:
plane:0.8879877475181992,
baseball-diamond:0.778076165652175,
bridge:0.46840197829363145,
ground-track-field:0.6584321304370467,
small-vehicle:0.7486973752001116,
large-vehicle:0.7496100108721503,
ship:0.8569940719256488,
tennis-court:0.9023482027992554,
basketball-court:0.7931574284009301,
storage-tank:0.8405654367548424,
soccer-ball-field:0.5659071452082054,
roundabout:0.6376705402201622,
harbor:0.577215213506967,
swimming-pool:0.6762310908612682,
helicopter:0.5404826062767616
The submitted information is :
Description: RetinaNet_DOTA_R3Det_DCL_B_2x_20201024_97.2w
Username: SJTU-Det
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""