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ai_paper_review's Introduction

Paper Reading

까먹으면 다시 보려고 정리합니다.

Classification

Object Detection

Segmentation

Self-supervised Learning

Video SSL

Semi-supervised Learning

weakly

Video Recognition

Video Segmentation

Zero Shot Classification

Zero Shot Detection

Zero Shot Segmentation

Few-Shot, Meta Learning

Prompting and Vision-Language Model

cv

nlp

Image Processing

3D Vision

NLP

GAN

Active Learning

Pose estimation

long tail

Face Recognition

Model Compression

Activation Function

Augmentation

Style Transfer

Regularization

Normalization

Optimization

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ai_paper_review's Issues

UserWarning 이 발생하는데 해결방법이 무엇일까요? 

C:\Users\jyyt0\anaconda3\envs\ilium\lib\site-packages\torch\nn\functional.py:3503: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
  "The default behavior for interpolate/upsample with float scale_factor changed "

VGGNet 모델 관련 질문

안녕하세요. VggNet 모델 관련해서 질문 드리고 싶은 것이 있어서 글을 작성합니다.

# 훈련 데이터셋과 검증 데이터셋 결합
combined_ds = torch.utils.data.ConcatDataset([train_tmp, val_tmp])

# 결합된 데이터셋을 다시 훈련 데이터셋과 검증 데이터셋으로 분할
# 전체 개수에서 %로 들어갈 수 있도록 조정
train_set_rete = 0.8
train_size = (int)(len(combined_ds) * train_set_rete)

val_size = len(combined_ds) - train_size
print("combined_ds :", len(combined_ds))
train_ds, val_ds = torch.utils.data.random_split(combined_ds, [train_size, val_size])

STL10이 좀 train쪽이 데이터가 적은 것 같아서 데이터를 합친 다음 다시 넣어서 돌려봤는데,

def forward(self, x):
        x = self.conv_layers(x)
        x = x.view(-1, 512 * 7 * 7)
        x = self.fcs(x)
        return x

x = x.view(-1, 512 * 7 * 7) -> 이 부분에서

shape '[-1, 25088]' is invalid for input of size 147456

와 같은 Error 가 발생합니다.

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