구차니의 잡동사니 모음

언제나 그렇듯(!) 1080은 돌리기 힘든 시대구만.. 저번에 찾아둔 걸로 버전 맞추고

-mvtec-ad 는 dataset 다운로드 받아 압축푼 최상위 디렉토리를 지정해주면 된다.

loss 만 보고 학습을 하게 했네. 

그래도 200 epoch 인데 내부는 어떤지 모르겠지만 10분 컷이면 양호하네

cpu 로 1개 코어만 사용해서 하는데 제법 걸린다. 이미지로 판별하고 출력할 수 없나?

tar 라면서 왜 zip 이래?!?

[링크 : https://github.com/gdwang08/STFPM]

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metric 함수. 얘도 함수였구나.

아무튼 metric은 격하게 오락가락 하고

[링크 : https://modulabs.co.kr/blog/loss-versus-accuracy]

loss 가 점진적 감소 -> 잘 학습

loss 급격히 감소 후 유지 -> 정상

loss 일정 시점 이후 증가 -> over fitting

[링크 : https://m.blog.naver.com/datapreprep/223748771141]

근데.. underfitting은 먼지 감이 안오네.

​   When to change the number of epochs (training cycles) Selecting the appropriate number of epochs is a balance between underfitting and overfitting. Underfitting: One of the most straightforward indicators of underfitting is if the model performs poorly on the training data. This can be observed in Edge Impulse Studio through metrics such as accuracy, or loss, depending on the type of problem (classification or regression). If these metrics indicate poor performance, it suggests that the model has not learned the patterns of the data well. In that case, increasing the number of epochs can improve your model performance. Please note that other solutions exist such as increasing your neural network architecture complexity, changing the preprocessing technique or reducing regularization. Overfitting: Detecting overfitting involves recognizing when the model has learned too much from the training data, including its noise and outliers, to the detriment of its performance on new, unseen data. Overfitting is characterized by the model performing exceptionally well on the training data but poorly on the validation or test data. Evaluating overfitting can be achieved by comparing the performance of the model between the training set and the validation set during training. When the performance on the validation set starts to degrade, it might indicate that the model is beginning to overfit the training data. In that case, decreasing the number of epochs can improve your model performance. As with underfitting, other solutions exist to reduce overfitting such as increasing the number of training data, adding regularization techniques to add penalties on large weights, adding dropout layers, simplifying the model architecture and even using early stopping.

[링크 : https://docs.edgeimpulse.com/knowledge/concepts/machine-learning/neural-networks/epochs]

overfitting 중인 loss 값 이라는데, 

학습셋이 너무 적을 경우 overfitting이 빠르게 발생하는 것으로 보인다.

그나저나 저 130 miliion parameter는 ssd300 에서 어떻게 산출된걸까?

[링크 : https://datascience.stackexchange.com/questions/46456/issues-with-training-ssd-on-own-dataset]

[링크 : https://www.mdpi.com/2673-6470/3/3/12]

MCUNet 보다 보니 NAS  라는게 보여서 찾아봄

[링크 : https://hanlab18.mit.edu/projects/tinyml/mcunet/assets/MCUNet-slides.pdf]

Neural Architecture Search 

[링크 : https://doing-ai.tistory.com/entry/Neural-Architecture-Search-NAS-Hardware-Aware-NAS-ProxylessNAS]

[링크 : https://seokdonge.tistory.com/27] ProxylessNAS

[링크 : https://ech97.tistory.com/entry/MnasNet] MnasNet

TuNAS

[링크 : https://seokdonge.tistory.com/26]

[링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Bender_Can_Weight_Sharing_Outperform_Random_Architecture_Search_An_Investigation_With_CVPR_2020_paper.pdf]

[링크 : https://github.com/google-research/google-research/tree/master/tunas]

STFPM -> PaSTe -> MCUNet 으로 찾아옴

[링크 : https://github.com/AMCO-UniPD/PaSTe]

MCU 에서 돌릴만큼 경량 네트워크

[링크 : https://cowkim-svd.tistory.com/7]

[링크 : https://github.com/mit-han-lab/mcunet]

[링크 : https://www.youtube.com/watch?v=YvioBgtec4U]

[링크 : https://www.youtube.com/watch?v=0pUFZYdoMY8]

[링크 : https://github.com/gdwang08/STFPM]

[링크 : https://blog.naver.com/kingjykim/223495332696]

[링크 : https://claude.ai/share/6d307c41-77bc-411e-90a8-982fb5ce0467]

STFPM	PaSTe

Student-Teacher Feature Pyramid Matching

[링크 : https://www.alphaxiv.org/ko/overview/2503.02691v1]

Partially Shared Teacher-student

[링크 : https://github.com/AMCO-UniPD/PaSTe]

[링크 : https://github.com/nelson1425/EfficientAD]

[링크 : https://github.com/krec7748/EfficientAD]

    [링크 : https://velog.io/@krec7748/논문-리뷰-및-구현-EfficientAD]

[링크 : https://techbless.github.io/2024/06/22/efficient-ad/]

[링크 : https://sj1226m.tistory.com/43]

VAD 분야 SOTA(state of the art. 별걸 다 줄여 -_-) 모델이라는데 무거워서 모바일이나 엣지에서 돌리긴 좀 무리일 듯.

[링크 : https://coderecording.tistory.com/9]

[링크 : https://github.com/amazon-science/patchcore-inspection]

[링크 : https://github.com/tiskw/patchcore-ad]

가장 만만한(?) 데이터 셋인 것 같은데

[링크 : https://hoya012.github.io/blog/MVTec-AD/]

5.3 GB(!!)

[링크 : https://www.kaggle.com/datasets/ipythonx/mvtec-ad]

[링크 : https://www.mvtec.com/research-teaching/datasets/mvtec-ad]

[링크 : https://huggingface.co/datasets/Voxel51/mvtec-ad]

5.9 GB(!!!)

[링크 : https://www.kaggle.com/datasets/mhskjelvareid/dagm-2007-competition-dataset-optical-inspection]

[링크 : https://github.com/M-3LAB/awesome-industrial-anomaly-detection]

비정상을 학습시키는게 아니라

정상만 학습시키고 정상에서 벗어나는 것을 탐지하는 기법

PaDiM

Ganomaly (GAN + Anomaly Detection)

[링크 : https://actionpower.medium.com/액션파워-lab-이미지에서-이상-영역-탐지-visual-anomaly-detection-는-어떻게-할까-6da65866366b]

PaDiM-Lite / PatchCore-Lite

[링크 : https://www.alphaxiv.org/ko/overview/2603.20288v1]

PaDiM (Patch Ditrubution Modeling)

[링크 : https://huggingface.co/papers/2011.08785]

[링크 : https://data-analysis-expertise.tistory.com/128]

[링크 : https://dippingtodeepening.tistory.com/110]

[링크 : https://cumulu-s.tistory.com/44]

VAD - Visual Anomaly Detection

MVTec-AD, MVTec-3D, MVTec-LOCO, VisA  데이터 셋

[링크 : https://velog.io/@barley_15/논문-리뷰-A-Survey-on-Visual-Anomaly-Detection-Challenge-Approach-and-Prospect]

PatchCore

[링크 : https://velog.io/@ljwljy51/PatchCore-Paper-Review]

PatchCore

SPADE , PaDiM

[링크 : https://ffighting.net/deep-learning-paper-review/anomaly-detection/patchcore/]

[링크 : https://github.com/PINTO0309/TensorflowLite-UNet]

[링크 : https://huggingface.co/qualcomm/Unet-Segmentation]

[링크 : https://github.com/milesial/Pytorch-UNet]

[링크 : https://github.com/Rumit95/Auto_Segmentation] semantic segmentation

[링크 : https://github.com/Rumit95/Semantic-Segmentation-of-Image]

[링크 : https://www.tensorflow.org/tutorials/images/segmentation?hl=ko]