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cfgs_res50_dota2.0_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 = 40000 * 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
DATASET_NAME = 'DOTA2.0'
CLASS_NUM = 18
# 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_DOTA2.0_R3Det_2x_20210410'
"""
R3Det
FLOPs: 1124094684; Trainable params: 41530106
This is your evaluation result for task 1:
mAP: 0.48433016270779095
ap of each class:
plane:0.7920200738879564,
baseball-diamond:0.4551905836447281,
bridge:0.38770671301480925,
ground-track-field:0.5746292874983392,
small-vehicle:0.4233527286939054,
large-vehicle:0.4946097693467611,
ship:0.5719757986261312,
tennis-court:0.7746529016429764,
basketball-court:0.5823408845309606,
storage-tank:0.5705764020949572,
soccer-ball-field:0.4358035856805696,
roundabout:0.5031340218974659,
harbor:0.3923683731983316,
swimming-pool:0.5448657101168235,
helicopter:0.5139043646487352,
container-crane:0.11816647583920607,
airport:0.4134305115134352,
helipad:0.16921474286414373
The submitted information is :
Description: RetinaNet_DOTA2.0_R3Det_2x_20210410_136w
Username: DetectionTeamCSU
Institute: UCAS
Emailadress: [email protected]
TeamMembers: yangxue
"""