DRIVE, Unet
train_num=20*9=180, batch_size=8;
test_num=20*9=180, batch_size=1
%run train_test_evaluate train --datasetname='DRIVE'
Epoch 1/5
----------
1/23,train_loss:0.736
2/23,train_loss:0.662
3/23,train_loss:0.623
4/23,train_loss:0.516
5/23,train_loss:0.492
6/23,train_loss:0.465
7/23,train_loss:0.460
8/23,train_loss:0.428
9/23,train_loss:0.387
10/23,train_loss:0.385
11/23,train_loss:0.368
12/23,train_loss:0.359
13/23,train_loss:0.377
14/23,train_loss:0.354
15/23,train_loss:0.359
16/23,train_loss:0.323
17/23,train_loss:0.330
18/23,train_loss:0.318
19/23,train_loss:0.315
20/23,train_loss:0.311
21/23,train_loss:0.309
22/23,train_loss:0.303
23/23,train_loss:0.319
epoch 1 loss:0.413
Epoch 2/5
----------
1/23,train_loss:0.293
2/23,train_loss:0.289
3/23,train_loss:0.277
4/23,train_loss:0.276
5/23,train_loss:0.266
6/23,train_loss:0.258
7/23,train_loss:0.273
8/23,train_loss:0.279
9/23,train_loss:0.255
10/23,train_loss:0.257
11/23,train_loss:0.249
12/23,train_loss:0.260
13/23,train_loss:0.252
14/23,train_loss:0.239
15/23,train_loss:0.269
16/23,train_loss:0.245
17/23,train_loss:0.234
18/23,train_loss:0.231
19/23,train_loss:0.232
20/23,train_loss:0.224
21/23,train_loss:0.236
22/23,train_loss:0.222
23/23,train_loss:0.213
epoch 2 loss:0.253
Epoch 3/5
----------
1/23,train_loss:0.217
2/23,train_loss:0.218
3/23,train_loss:0.238
4/23,train_loss:0.211
5/23,train_loss:0.210
6/23,train_loss:0.208
7/23,train_loss:0.205
8/23,train_loss:0.208
9/23,train_loss:0.212
10/23,train_loss:0.193
11/23,train_loss:0.203
12/23,train_loss:0.234
13/23,train_loss:0.197
14/23,train_loss:0.201
15/23,train_loss:0.193
16/23,train_loss:0.234
17/23,train_loss:0.185
18/23,train_loss:0.185
19/23,train_loss:0.209
20/23,train_loss:0.198
21/23,train_loss:0.184
22/23,train_loss:0.180
23/23,train_loss:0.174
epoch 3 loss:0.204
Epoch 4/5
----------
1/23,train_loss:0.192
2/23,train_loss:0.189
3/23,train_loss:0.183
4/23,train_loss:0.183
5/23,train_loss:0.195
6/23,train_loss:0.173
7/23,train_loss:0.174
8/23,train_loss:0.186
9/23,train_loss:0.185
10/23,train_loss:0.164
11/23,train_loss:0.177
12/23,train_loss:0.169
13/23,train_loss:0.156
14/23,train_loss:0.164
15/23,train_loss:0.158
16/23,train_loss:0.163
17/23,train_loss:0.164
18/23,train_loss:0.159
19/23,train_loss:0.165
20/23,train_loss:0.179
21/23,train_loss:0.157
22/23,train_loss:0.191
23/23,train_loss:0.169
epoch 4 loss:0.174
Epoch 5/5
----------
1/23,train_loss:0.156
2/23,train_loss:0.184
3/23,train_loss:0.152
4/23,train_loss:0.164
5/23,train_loss:0.150
6/23,train_loss:0.155
7/23,train_loss:0.144
8/23,train_loss:0.181
9/23,train_loss:0.159
10/23,train_loss:0.179
11/23,train_loss:0.175
12/23,train_loss:0.148
13/23,train_loss:0.150
14/23,train_loss:0.155
15/23,train_loss:0.148
16/23,train_loss:0.157
17/23,train_loss:0.139
18/23,train_loss:0.157
19/23,train_loss:0.149
20/23,train_loss:0.156
21/23,train_loss:0.175
22/23,train_loss:0.132
23/23,train_loss:0.134
epoch 5 loss:0.157
%run train_test_evaluate test --datasetname='DRIVE'
index:1 dice score:0.8297099055045265
index:2 dice score:0.8287949542118989
index:3 dice score:0.7990722446525212
index:4 dice score:0.7737353239599942
index:5 dice score:0.7501107665042092
index:6 dice score:0.736418905667778
index:7 dice score:0.7807346326836582
index:8 dice score:0.811734443900049
index:9 dice score:0.7925742574257426
index:10 dice score:0.7724620770128354
index:11 dice score:0.8142943184099578
index:12 dice score:0.8366117531349889
index:13 dice score:0.752596874717731
index:14 dice score:0.7819158791921438
index:15 dice score:0.8407057446274772
index:16 dice score:0.790240760125614
index:17 dice score:0.8055893074119077
index:18 dice score:0.8419537472411477
index:19 dice score:0.8048796658489691
index:20 dice score:0.7675104448267388
index:21 dice score:0.7180045531030387
index:22 dice score:0.7561423873089572
index:23 dice score:0.6703225806451613
index:24 dice score:0.6476319089418346
index:25 dice score:0.7377244213714038
index:26 dice score:0.7472294926769505
index:27 dice score:0.7632150615496017
index:28 dice score:0.8383145516903479
index:29 dice score:0.8286048932847475
index:30 dice score:0.7994041064835805
index:31 dice score:0.7959802201308024
index:32 dice score:0.8460909388289173
index:33 dice score:0.8226716645384521
index:34 dice score:0.6729400410182169
index:35 dice score:0.8071216617210683
index:36 dice score:0.8198539869157107
index:37 dice score:0.8110170138094148
index:38 dice score:0.810272134917593
index:39 dice score:0.7870073624945864
index:40 dice score:0.7710793879018178
index:41 dice score:0.7117437722419929
index:42 dice score:0.6714681440443213
index:43 dice score:0.7521124856350977
index:44 dice score:0.7454962787786047
index:45 dice score:0.7106001629401647
index:46 dice score:0.7016634851377147
index:47 dice score:0.7469930365055919
index:48 dice score:0.7577943513877002
index:49 dice score:0.6237424547283702
index:50 dice score:0.6715496666940992
index:51 dice score:0.737471663531568
index:52 dice score:0.6584392919471453
index:53 dice score:0.7385019710906702
index:54 dice score:0.7957613734780172
index:55 dice score:0.7159673175460463
index:56 dice score:0.7263157894736842
index:57 dice score:0.7859102064385737
index:58 dice score:0.6218989479153136
index:59 dice score:0.6277504270069326
index:60 dice score:0.7933511614493209
index:61 dice score:0.7073670585697336
index:62 dice score:0.7300138538016462
index:63 dice score:0.8008331456879081
index:64 dice score:0.6968174204355109
index:65 dice score:0.7107327658153898
index:66 dice score:0.7116939890710382
index:67 dice score:0.5217391304347826
index:68 dice score:0.5316423589093215
index:69 dice score:0.6872949501447411
index:70 dice score:0.6649980239757608
index:71 dice score:0.7245220282626766
index:72 dice score:0.7662030006681649
index:73 dice score:0.7430568328122126
index:74 dice score:0.7651911788037442
index:75 dice score:0.7073257048484134
index:76 dice score:0.7066019295037518
index:77 dice score:0.6698645598194131
index:78 dice score:0.6029132362254591
index:79 dice score:0.6875153902979562
index:80 dice score:0.6874564459930314
index:81 dice score:0.6774159553166825
index:82 dice score:0.7912149795860904
index:83 dice score:0.8141836173721825
index:84 dice score:0.8191653786707882
index:85 dice score:0.6580751302440362
index:86 dice score:0.7039848197343453
index:87 dice score:0.7602943654850851
index:88 dice score:0.6683537897001138
index:89 dice score:0.7610169491525424
index:90 dice score:0.788251441119956
index:91 dice score:0.8344713915705204
index:92 dice score:0.7584920956627482
index:93 dice score:0.7026369168356998
index:94 dice score:0.8349673202614379
index:95 dice score:0.7376069756176328
index:96 dice score:0.6556103108415466
index:97 dice score:0.8137147284819526
index:98 dice score:0.7343868841375814
index:99 dice score:0.7167332445628052
index:100 dice score:0.7576724772894672
index:101 dice score:0.7737153445941131
index:102 dice score:0.7791081750619323
index:103 dice score:0.7381283298586981
index:104 dice score:0.6950860608222599
index:105 dice score:0.6792710706150342
index:106 dice score:0.7914163090128755
index:107 dice score:0.7807892149978936
index:108 dice score:0.7079326923076923
index:109 dice score:0.6867088607594937
index:110 dice score:0.766517921404923
index:111 dice score:0.78824095061474
index:112 dice score:0.6331141661685594
index:113 dice score:0.7044676276180616
index:115 dice score:0.747887323943662
index:116 dice score:0.7850209781451563
index:117 dice score:0.7975151452921244
index:118 dice score:0.7663934426229508
index:119 dice score:0.7870156734823773
index:120 dice score:0.7933170099380671
index:121 dice score:0.6776306992913491
index:122 dice score:0.6527592231014284
index:123 dice score:0.7640910548252645
index:124 dice score:0.6801795280150572
index:125 dice score:0.710610002891009
index:126 dice score:0.8234587189039779
index:127 dice score:0.8329795918367346
index:128 dice score:0.8262011026516146
index:129 dice score:0.7653206650831353
index:130 dic
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医学影像作业 基于医学影像配准+DUNet实现的视网膜血管检测_眼底血管分割源码+数据集+实验报告.zip 图像配准 眼底血管分割实验 详细操作说明 实验报告 【实验思路】 1.图像预处理: 单通道化RGB2Gray 归一化 对比度限制自适应直方图均衡化 伽马校正 2.图像分割成小块patch 3.torch写网络 Unet  - Unet++  4.训练与测试,计算每个小patch的train_loss和dice_score 5.合并图像 6.计算整体测度 【实验结果】 CHASE数据集用cuda训练batchsize为2,网络采用UNet++,轮数epoch=5,测试集结果:avarage Dice: **78.03%**, avarage Accuracy: **96.91%** DRIVE数据集用cpu训练batchsize为8,网络采用UNet,轮数epoch=5,测试集结
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