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cfgs_res50_dota1.5_fcos_v2.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(','))
LR = 1e-3 * BATCH_SIZE * NUM_GPU
SAVE_WEIGHTS_INTE = 32000
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 = 'DOTA1.5'
CLASS_NUM = 16
# 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_DOTA1.5_RSDet_1x_20210617'
"""
FCOS + modulated loss
FLOPs: 468525597; Trainable params: 32092441
This is your evaluation result for task 1:
mAP: 0.621811745576584
ap of each class:
plane:0.7938999239367199,
baseball-diamond:0.740065627981387,
bridge:0.4496195731750868,
ground-track-field:0.6093950686287898,
small-vehicle:0.5643574518958718,
large-vehicle:0.6058184462449729,
ship:0.7801808948640423,
tennis-court:0.8946595109705113,
basketball-court:0.7087307071702325,
storage-tank:0.735681567575362,
soccer-ball-field:0.5121123427253175,
roundabout:0.6833713876024533,
harbor:0.5623638432683176,
swimming-pool:0.6829058209388849,
helicopter:0.5228573021360805,
container-crane:0.10296846011131726
The submitted information is :
Description: FCOS_DOTA1.5_RSDet_1x_20210617_41.6w
Username: SJTU-Det
Institute: SJTU
Emailadress: [email protected]
TeamMembers: yangxue
"""