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cfgs_res50_dota2.0_v5.py
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cfgs_res50_dota2.0_v5.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
GPU_GROUP = "0,1"
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
# 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 / 5.0
REG_LOSS_MODE = 1 # IoU-Smooth L1
VERSION = 'RetinaNet_DOTA2.0_2x_20210427'
"""
RetinaNet-H + IoU-Smooth L1
FLOPs: 676601855; Trainable params: 33148131
This is your evaluation result for task 1:
mAP: 0.46306689567660153
ap of each class:
plane:0.7754947865172579,
baseball-diamond:0.46434700034028786,
bridge:0.39599890161126844,
ground-track-field:0.5841579611023893,
small-vehicle:0.347077308109511,
large-vehicle:0.36984795081268984,
ship:0.47937500010188855,
tennis-court:0.7713614052919219,
basketball-court:0.5712051108571043,
storage-tank:0.5361292205829321,
soccer-ball-field:0.4011674247207251,
roundabout:0.5077467772636136,
harbor:0.4002952079323899,
swimming-pool:0.5416185098631832,
helicopter:0.5162148043881953,
container-crane:0.13870523708490112,
airport:0.4057909924349168,
helipad:0.1286705231636533
The submitted information is :
Description: RetinaNet_DOTA2.0_2x_20210427_104w
Username: yangxue
Institute: UCAS
Emailadress: yangxue16@mails.ucas.ac.cn
TeamMembers: yangxue
"""