best single model of RSNA

对于[1]中的个单模型进行汇总:

 

用户	模型	数据集像素	LB得分	备注
Tim Yee	EfficientNet B1	224x224	0.098
Tim Yee	EfficientNet B0	224x224	0.093
Kun Jiang	VGG19,epoch20	没说	0.082
XingJian Lyu	EfficientNet B0	224x224	0.073	I grouped on patient and used some tricks, though   Solved it via grouping via patients. batch_size是48  使用一些技巧可以到达0.066
Yifeng (Ethan) Zou	EfficientNet B0	320x320	0.079	With raw input from dcmread, random ShuffleSplit, no tta,（续）
Yifeng (Ethan) Zou	EfficientNet B4	224x224	0.080	(接上)I'm pretty sure that's subjective to many other factors. For .95/.05 split, default class weight it seems 4-6 epochs works well and then starts to overfit real bad real fast afterwards.
Yifeng (Ethan) Zou	ResNet50	224x224	0.098
takuoko	Se_resnext50	224x224	0.082	他推荐了： https://www.kaggle.com/dcstang/see-like-a-radiologist-with-systematic-windowing
4ui_iurz1	EfficientNet B0	256x256	0.072	png 5epochs pytorch
Appian	se_resnext50_32x4d	224x224	0.074	2 epochs hflip, crop
William Green	Resnet50	没说	0.094	w/o any augmentation or tta
hi	InceptionResnetv2	224x224	0.086
Fernando Camargo	VGG19	224x224	0.073	20 epochs.   40min/epoch
Salil Mishra				提到了一篇加速训练的论文
Alimbekov Renat [dsmlkz]	ResNeXt 32x8d -		0.087 with	50/50 % sampler
Jayaram	EfficientNet B0	512x512	0.078	Random split 95% train & 5% validation… trained for 10 epochs（目测过拟合）
KeepLearning	inceptionV3	224x224	0.079
DrHB	EfficientNet B0	224x224	0.080	CV: 1 Fold AUG: [zoom, rotate] TTA: No PRETRAINED: True EPOCH: 20 LR: 1e-3
akensert	EfficientNet B0	224x224	0.079
Ian Pan	EfficientNet B5	512x512	0.070	是上一次RSNA的金牌得主 100 epochs, 16,000 images per epoch About 60-65 hours.
nan	resnet34	256x256	0.078	第二个epoch虽然本地得分上升，但是lb上下降
Igor Krashenyi	Custom model	512x512	0.069
OrKatz	inceptionv4		0.078
Arijit Gupta	ResNeXt-101，32x16d		0.086
Abhilash Awasthi	resnet34	512 x 512	0.084
Salil Mishra	Efficient Net B4	256x256		10 epochs 256x256 - 0.113 3 epochs 256x256 - 0.107
Joe England	EfficientNet B0	256x256	0.077	Image augmentation included horizontal flip and rotation of up to 10 degrees Trained for 10 epochs using cyclical learning rate, max 0.009
William Green	Resnet50	256x256	0.089
Yaroslav Isaienkov	ResNet50	224*224	0.108
Rajnish Chauhan	EfficientNet b2	224x224	0.107	With just 2 epoch . steps = len(generator) Little touch on image pre processing for Gaussian Blur loss - log_loss
smerllo	Inception	224x224	0.091

tta:Test Time Augmentation

#-------------------------------------------

统计情况：

0.072:efficientnet b0

0.073:efficientnet b0,vgg19

0.074:se_resnext50_32x4d

resnet50公认不行

0.077:efficientnet b0

0.078:efficientnet b0,resnet34,inceptionv4

0.079:efficientnet b0,inceptionV3

#-----------------------更新0.07以下的--------------------

Single-fold SE ResNext50, 512x512 raw HU image: lb 0.066 w/o tta

EfficientNet-B0　224x224　Public LB: 0.069 without TTA

 

efficientnet-b2 256x256　w/o 3 windowing preprocess　publicLB: 0.069

 

 

 

 

Reference:

[1]https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/discussion/110221#latest-647044

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