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cfgs_res50_dota2.0_fcos_v2.py
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# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
from configs._base_.models.retinanet_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"
NUM_GPU = len(GPU_GROUP.strip().split(','))
LR = 1e-3 * BATCH_SIZE * NUM_GPU
SAVE_WEIGHTS_INTE = 40000
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
CTR_WEIGHT = 1.0
REG_LOSS_MODE = 0
VERSION = 'FCOS_DOTA2.0_RSDet_1x_20210617'
"""
FCOS + modulated loss
FLOPs: 468608575; Trainable params: 32097051
This is your evaluation result for task 1:
mAP: 0.4881184216403336
ap of each class:
plane:0.7863183785113876,
baseball-diamond:0.4707151264649854,
bridge:0.39470207745499414,
ground-track-field:0.546908617302247,
small-vehicle:0.485864491468598,
large-vehicle:0.46568955587333427,
ship:0.5621718190273812,
tennis-court:0.7660422340867759,
basketball-court:0.5619650407916696,
storage-tank:0.6488520700802246,
soccer-ball-field:0.40844718805430663,
roundabout:0.5262690391113736,
harbor:0.43593581191541636,
swimming-pool:0.5588789927215072,
helicopter:0.4454172078637718,
container-crane:0.06500235515779557,
airport:0.4830832997332654,
helipad:0.17386828390696976
The submitted information is :
Description: FCOS_DOTA2.0_RSDet_1x_20210617_52w
Username: sjtu-deter
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""