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cfgs_res50_dota_v5.py
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cfgs_res50_dota_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"
NUM_GPU = len(GPU_GROUP.strip().split(','))
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')
# loss
CLS_WEIGHT = 1.0
REG_WEIGHT = 1.0 / 5.0
REG_LOSS_MODE = 1 # IoU-Smooth L1
VERSION = 'RetinaNet_DOTA_1x_20201225'
"""
RetinaNet-H + IoU-Smooth L1
FLOPs: 484911740; Trainable params: 33002916
This is your result for task 1:
mAP: 0.6699231893137383
ap of each class:
plane:0.8833785173522034,
baseball-diamond:0.7627482529936743,
bridge:0.44320593902405797,
ground-track-field:0.6785841556477691,
small-vehicle:0.6303299319074853,
large-vehicle:0.5124927246071527,
ship:0.7277748791449373,
tennis-court:0.8980387801428189,
basketball-court:0.79974279949969,
storage-tank:0.7797862635611005,
soccer-ball-field:0.5409846307060925,
roundabout:0.632179992142947,
harbor:0.5621019025063557,
swimming-pool:0.6734955940136754,
helicopter:0.5240034764561138
The submitted information is :
Description: RetinaNet_DOTA_1x_20201225_45.9w
Username: SJTU-Det
Institute: SJTU
Emailadress: yangxue-2019-sjtu@sjtu.edu.cn
TeamMembers: yangxue
"""