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테스트 데이터 세트에 Inference 및 Evaluation 적용하기
- 런타임 다시 시작 수행 필요(버그?)
- tools/test.py 스크립트는 colab에서 오류 발생
- 테스트용 Dataset과 DataLoader생성하고 single_gpu_test()를 호출하여 inference 결과를 반환. batch_size를 1로 설정하지 않으면 single_gpu_test() 오류 발생.
### 아래는 런타임 다시 시작 후 실행
from mmcv import Config
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.coco import CocoDataset
from mmdet.apis import set_random_seed
@DATASETS.register_module(force=True)
class BCCDDataset(CocoDataset):
CLASSES = ('WBC', 'RBC', 'Platelets')
config_file = '/content/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
cfg = Config.fromfile(config_file)
# dataset에 대한 환경 파라미터 수정.
cfg.dataset_type = 'BCCDDataset'
cfg.data_root = '/content/BCCD_Dataset/BCCD/'
# train, val, test dataset에 대한 type, data_root, ann_file, img_prefix 환경 파라미터 수정.
cfg.data.train.type = 'BCCDDataset'
cfg.data.train.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.train.ann_file = 'train.json'
cfg.data.train.img_prefix = 'JPEGImages'
cfg.data.val.type = 'BCCDDataset'
cfg.data.val.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.val.ann_file = 'val.json'
cfg.data.val.img_prefix = 'JPEGImages'
cfg.data.test.type = 'BCCDDataset'
cfg.data.test.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.test.ann_file = 'test.json'
cfg.data.test.img_prefix = 'JPEGImages'
# class의 갯수 수정.
cfg.model.roi_head.bbox_head.num_classes = 3
# pretrained 모델
cfg.load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
# 학습 weight 파일로 로그를 저장하기 위한 디렉토리 설정.
cfg.work_dir = './tutorial_exps'
# 학습율 변경 환경 파라미터 설정.
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 10
# CocoDataset의 경우 metric을 bbox로 설정해야 함.(mAP아님. bbox로 설정하면 mAP를 iou threshold를 0.5 ~ 0.95까지 변경하면서 측정)
cfg.evaluation.metric = 'bbox'
cfg.evaluation.interval = 12
cfg.checkpoint_config.interval = 12
# bug(?)로 인해 test용 dataset evaluation 시 1로 설정. data loader에서 GPU갯수별 Batch size 임.
cfg.data.samples_per_gpu = 1
# 두번 config를 로드하면 lr_config의 policy가 사라지는 오류로 인하여 설정.
cfg.lr_config.policy='step'
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
print(cfg.pretty_text)
#model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'BCCDDataset'
data_root = '/content/BCCD_Dataset/BCCD/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=2,
train=dict(
type='BCCDDataset',
ann_file='train.json',
img_prefix='JPEGImages',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
],
data_root='/content/BCCD_Dataset/BCCD/'),
val=dict(
type='BCCDDataset',
ann_file='val.json',
img_prefix='JPEGImages',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
data_root='/content/BCCD_Dataset/BCCD/'),
test=dict(
type='BCCDDataset',
ann_file='test.json',
img_prefix='JPEGImages',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
data_root='/content/BCCD_Dataset/BCCD/'))
evaluation = dict(interval=12, metric='bbox')
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup=None,
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=12)
log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = './tutorial_exps'
seed = 0
gpu_ids = range(0, 1)cfg.dump('/content/tutorial_exps/bccd_faster_rcnn_conf.py')!mkdir -p /content/show_test_output# tools/test.py 는 colab에서 제대로 동작하지 않음.
%cd /content/mmdetection
!python tools/test.py /content/tutorial_exps/bccd_faster_rcnn_conf.py /content/tutorial_exps/epoch_12.pth \
--eval 'bbox' \
--show-dir /content/show_test_output
# /content/mmdetection
# Traceback (most recent call last):
# File "tools/test.py", line 238, in <module>
# main()
# File "tools/test.py", line 177, in main
# dataset = build_dataset(cfg.data.test)
# File "/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/datasets/builder.py", line 78, in build_dataset
# dataset = build_from_cfg(cfg, DATASETS, default_args)
# File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 45, in build_from_cfg
# f'{obj_type} is not in the {registry.name} registry')
# KeyError: 'BCCDDataset is not in the dataset registry'테스트용 dataset와 dataloader를 별도로 설정하고 trained된 checkpoint 모델을 로딩하여 test 수행.
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTensor)
# test용 Dataset과 DataLoader 생성.
# build_dataset()호출 시 list로 감싸지 않는 것이 train용 dataset 생성시와 차이.
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
# 반드시 아래 samples_per_gpu 인자값은 1로 설정
samples_per_gpu=cfg.data.samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False)
# 반드시 아래 코드에서 'img' 키값이 tensor로 출력되어야 함.
next(iter(data_loader))
# loading annotations into memory...
# Done (t=0.01s)
# creating index...
# index created!
# {'img': [tensor([[[[0.9474, 0.9474, 0.9474, ..., 0.0000, 0.0000, 0.0000],
# [0.9474, 0.9474, 0.9474, ..., 0.0000, 0.0000, 0.0000],
# [0.9646, 0.9646, 0.9646, ..., 0.0000, 0.0000, 0.0000],
# ...,
# [1.1700, 1.1700, 1.1700, ..., 0.0000, 0.0000, 0.0000],
# [1.1700, 1.1700, 1.1700, ..., 0.0000, 0.0000, 0.0000],
# [1.1700, 1.1700, 1.1700, ..., 0.0000, 0.0000, 0.0000]],
#
# [[1.2031, 1.2031, 1.2031, ..., 0.0000, 0.0000, 0.0000],
# [1.2031, 1.2031, 1.2031, ..., 0.0000, 0.0000, 0.0000],
# [1.2206, 1.2206, 1.2206, ..., 0.0000, 0.0000, 0.0000],
# ...,
# [1.5007, 1.5007, 1.5007, ..., 0.0000, 0.0000, 0.0000],
# [1.5007, 1.5007, 1.5007, ..., 0.0000, 0.0000, 0.0000],
# [1.5007, 1.5007, 1.5007, ..., 0.0000, 0.0000, 0.0000]],
#
# [[1.3851, 1.3851, 1.3851, ..., 0.0000, 0.0000, 0.0000],
# [1.3851, 1.3851, 1.3851, ..., 0.0000, 0.0000, 0.0000],
# [1.4025, 1.3851, 1.3677, ..., 0.0000, 0.0000, 0.0000],
# ...,
# [1.5768, 1.5768, 1.5768, ..., 0.0000, 0.0000, 0.0000],
# [1.5768, 1.5768, 1.5768, ..., 0.0000, 0.0000, 0.0000],
# [1.5768, 1.5768, 1.5768, ..., 0.0000, 0.0000, 0.0000]]]])],
# 'img_metas': [DataContainer([[{'filename': '/content/BCCD_Dataset/BCCD/JPEGImages/BloodImage_00007.jpg', 'ori_filename': 'BloodImage_00007.jpg', 'ori_shape': (480, 640, 3), 'img_shape': (800, 1067, 3), 'pad_shape': (800, 1088, 3), 'scale_factor': array([1.6671875, 1.6666666, 1.6671875, 1.6666666], dtype=float32), 'flip': False, 'flip_direction': None, 'img_norm_cfg': {'mean': array([123.675, 116.28 , 103.53 ], dtype=float32), 'std': array([58.395, 57.12 , 57.375], dtype=float32), 'to_rgb': True}}]])]}from mmdet.apis import inference_detector, init_detector, show_result_pyplot
checkpoint_file = '/content/tutorial_exps/epoch_12.pth'
# checkpoint 저장된 model 파일을 이용하여 모델을 생성, 이때 Config는 위에서 update된 config 사용.
model_ckpt = init_detector(cfg, checkpoint_file, device='cuda:0')
# /usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator``
# '``build_anchor_generator`` would be deprecated soon, please use '
# Use load_from_local loaderfrom mmdet.apis import multi_gpu_test, single_gpu_test
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
model_ckpt = MMDataParallel(model_ckpt, device_ids=[0])
# single_gpu_test() 를 호출하여 test데이터 세트의 interence 수행. 반드시 batch size는 1이 되어야 함.
# 위에서 만든 /content/show_test_output 디렉토리에 interence 결과가 시각화된 이미지가 저장됨.
outputs = single_gpu_test(model_ckpt, data_loader, True, '/content/show_test_output', 0.3)batch 크기를 1보다 키우고 테스트 수행 하려면
- 만일 batch size를 1보다 크게 해서 설정하려면 cfg.data.samples_per_gpu 값을 1보다 크게 설정하고 single_gpu_test() 함수의 bug(?) 코드를 수정 필요.
- 다시 런타임 다시 시작을 실행 후 config 부터 재 설정후 아래 코드 수행 필요. 여기서는 수행하지 않는 걸로 스킵
'''
import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
def single_gpu_test_batch(model,
data_loader,
show=False,
out_dir=None,
show_score_thr=0.3):
model.eval()
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
batch_size = len(result)
if show or out_dir:
if batch_size == 1 and isinstance(data['img'][0], torch.Tensor):
img_tensor = data['img'][0]
else:
# 아래 코드를 주석 처리 필요. batch_size가 1보다 클때는 data['img'][0].data[0]가 아니라 data['img'][0].data 가 되어야 함.
#img_tensor = data['img'][0].data[0]
# 아래는 버그(?) 수정 코드
img_tensor = data['img'][0].data
print('img_tensor shape2:', img_tensor.shape)
img_metas = data['img_metas'][0].data[0]
imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
assert len(imgs) == len(img_metas)
for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
h, w, _ = img_meta['img_shape']
img_show = img[:h, :w, :]
ori_h, ori_w = img_meta['ori_shape'][:-1]
img_show = mmcv.imresize(img_show, (ori_w, ori_h))
if out_dir:
out_file = osp.join(out_dir, img_meta['ori_filename'])
else:
out_file = None
model.module.show_result(
img_show,
result[i],
show=show,
out_file=out_file,
score_thr=show_score_thr)
# encode mask results
if isinstance(result[0], tuple):
result = [(bbox_results, encode_mask_results(mask_results))
for bbox_results, mask_results in result]
results.extend(result)
for _ in range(batch_size):
prog_bar.update()
return results
from mmdet.apis import multi_gpu_test, single_gpu_test
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
model_ckpt = MMDataParallel(model_ckpt, device_ids=[0])
# single_gpu_test() 를 호출하여 test데이터 세트의 interence 수행. 반드시 batch size는 1이 되어야 함.
outputs = single_gpu_test(model_ckpt, data_loader, True, '/content/tutorial_exps', 0.3)
'''반환된 test용 데이터세트의 inference 적용 결과 확인 및 성능 evaluation 수행.
print('결과 outputs type:', type(outputs))
print('evalution 된 파일의 갯수:', len(outputs))
print('첫번째 evalutation 결과의 type:', type(outputs[0]))
print('첫번째 evaluation 결과의 CLASS 갯수:', len(outputs[0]))
print('첫번째 evaluation 결과의 CLASS ID 0의 type과 shape', type(outputs[0][0]), outputs[0][0].shape)
# 결과 outputs type: <class 'list'>
# evalution 된 파일의 갯수: 72
# 첫번째 evalutation 결과의 type: <class 'list'>
# 첫번째 evaluation 결과의 CLASS 갯수: 3
# 첫번째 evaluation 결과의 CLASS ID 0의 type과 shape <class 'numpy.ndarray'> (1, 5)print(outputs)
# [[array([[200.52539 , 92.739204 , 418.35672 , 284.60696 ,
0.99753225]], dtype=float32), array([[1.7161997e+01, 2.9421707e+02, 1.3896806e+02, 3.9979282e+02,
9.7877252e-01],
[4.9222028e+02, 2.9748608e+02, 6.0555103e+02, 4.0151849e+02,
9.5355266e-01],
[5.3456342e+02, 3.6799695e+02, 6.4000000e+02, 4.7710013e+02,
9.1202462e-01],
[3.1212755e+01, 1.2918649e+02, 1.3549530e+02, 2.3172231e+02,
8.3944076e-01],
[1.6357803e+02, 2.6890323e+02, 2.6914523e+02, 3.8934702e+02,
8.2909942e-01],
[4.9686783e+02, 1.6922240e+02, 6.1225970e+02, 2.9487891e+02,
7.9936832e-01],
[3.2403314e+02, 3.0470923e+02, 4.5897992e+02, 4.3507742e+02,
7.3746300e-01],
[4.0063834e+02, 1.7505469e+02, 4.9044974e+02, 2.7783981e+02,
3.9149642e-01],
[1.7672256e+02, 4.6024315e+01, 2.9580939e+02, 1.4779662e+02,
3.6525750e-01],
[1.7738934e+01, 3.6607216e+01, 1.3155940e+02, 1.4271782e+02,
2.8692830e-01],
[5.1438306e+02, 3.7719646e+01, 6.2653558e+02, 1.4797528e+02,
2.6563898e-01],
[6.7035019e+01, 3.6262000e+02, 1.8478546e+02, 4.7663516e+02,
2.5038856e-01],
[2.6938425e+02, 3.3076352e+02, 3.4397900e+02, 4.1794055e+02,
2.4135420e-01],
[2.2859131e+02, 3.2165677e+02, 3.2059082e+02, 4.1296759e+02,
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[5.24794861e+02, 2.16390015e+02, 6.34772888e+02, 3.09650909e+02,
9.20614600e-01],
[3.93202332e+02, 0.00000000e+00, 5.33183228e+02, 8.24931564e+01,
8.83027792e-01],
[5.12031860e+02, 9.35096741e+01, 6.08654297e+02, 1.95551285e+02,
8.49205673e-01],
[4.62535187e+02, 1.18474678e+02, 5.56261780e+02, 2.10281784e+02,
8.06960225e-01],
[3.95888367e+02, 1.30046082e+02, 5.03545013e+02, 2.32906433e+02,
8.04393351e-01],
[1.94942200e+02, 1.55126785e+02, 2.99493805e+02, 2.40399338e+02,
7.11985588e-01],
[1.57450211e+02, 2.34780334e+02, 2.74563599e+02, 3.58703217e+02,
7.05446184e-01],
[1.63177917e+02, 1.07295906e+02, 2.73954742e+02, 1.99046021e+02,
6.82574332e-01],
[2.67495209e+02, 2.68807648e+02, 3.81505524e+02, 3.79569916e+02,
6.41149223e-01],
[5.74545410e+02, 1.44370747e+01, 6.38489746e+02, 1.17637337e+02,
1.59230113e-01],
[1.46026077e+02, 2.40103668e+02, 2.31544617e+02, 3.32396851e+02,
9.46123302e-02]], dtype=float32), array([[ 66.85258 , 109.761696 , 105.98781 , 147.87102 , 0.9730454],
[115.90851 , 21.080624 , 162.34404 , 62.225098 , 0.9634706],
[418.65884 , 227.71802 , 480.53766 , 293.78223 , 0.9494921],
[ 0. , 246.36829 , 36.47911 , 284.09985 , 0.8202262]],
dtype=float32)]]metric = dataset.evaluate(outputs, metric='bbox')
print(metric)
# Evaluating bbox...
# Loading and preparing results...
# DONE (t=0.00s)
# creating index...
# index created!
# Running per image evaluation...
# Evaluate annotation type *bbox*
# DONE (t=0.81s).
# Accumulating evaluation results...
# DONE (t=0.07s).
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.627
# Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.920
# Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.736
# Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.615
# Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.499
# Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.475
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.711
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.711
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.711
# Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.640
# Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.625
# Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.523
# OrderedDict([('bbox_mAP', 0.627), ('bbox_mAP_50', 0.92), ('bbox_mAP_75', 0.736), ('bbox_mAP_s', 0.615), ('bbox_mAP_m', 0.499), ('bbox_mAP_l', 0.475), ('bbox_mAP_copypaste', '0.627 0.920 0.736 0.615 0.499 0.475')])반응형
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