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wonchul-kim committed Jan 6, 2024
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8 changes: 0 additions & 8 deletions _posts/023-02-11-computer-vision-peft.md.md

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---
layout: post
title: MongoDB
category: Database
tag: DB, RMS
category: Backend
tag: mongodb
---

#### Check system version
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layout: post
title: postgres & pgadmin
category: Database
tag: DB, RMS
category: Backend
tag: postgreql
---

## Run postgresql & pgadmin with docker-compose
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---
layout: post
title: django
category: Django
tag: django orm
title: Django ORM
category: Backend
tag: django
---

## DJANGO ORM
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layout: post
title: SQLAlchemy - relationship
category: Database
tag: sqlalchemy relationship
category: Backend
tag: sqlalchemy
---

1. 1:N
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layout: post
title: Create Table in pgadmin
category: Database
tag: pgadmin dql
category: Backend
tag: pgadmin sql
---

### Using query tool,
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layout: post
title: Train YOLO v4 using darknet with custom datasets
category: Computer Vision
tag: yolo v4
tag: [object detection]
---

### Install darknet
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layout: post
title: Bag of Tricks
category: Lectures
tag: tricks
category: Computer Vision
tag: [object detection]
---

인공지능과 관련된 많은 논문들이 정확도를 0.01%라도 개선하기 위해서 엄청난 computation time과 cost를 통해서 노력을 하고 있는 것에 반해, [An End-to-End Deep Learning Benchmark and Competition - DAWNBench](https://dawn.cs.stanford.edu/benchmark/)에서는 **training time, training cost, inference latency, inference cost**에 중점을 두고 있다.
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layout: post
title: Metrics for evaluating algorithms
category: Lectures
category: Computer Vision
tag: metrics
---

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layout: post
title: augmentations
title: Augmentations for Object Detection
category: Computer Vision
tag: augmentations
tag: augmentation
---

일반적인 딥러닝 라이브러리에서 제공하는 augmentation function이외에 실제로 data를 직접 augmentation하는 과정에서는
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layout: post
title: Augmentations for Object Detection
category: Computer Vision
tag: classification
---

#### Corase-grained classification vs. Fine-grained classification

* Coarse-grained classification: 하나의 class 내부에 대해서는 분류를 하지 않고 동일한 class로 분류
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layout: post
title: For small objects
category: Computer Vision
tag: small objects
tag: object detection
---

# To improve performance on small objects
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---
layout: post
title: albumentations
category: augmentation
title: Albumentations
category: Computer Vision
tag: albumentations
---

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layout: post
title: albumentations Lambda
category: augmentation
tag: albumentations
title: Albumentations Lambda
category: Computer Vision
tag: albumentation
---
```python
import os.path as osp
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layout: post
title: Albumentations - TpTensor
category: Augmentation
tag: albumentations, totensor
title: ToTensor
category: Computer Vision
tag: albumentations
---

## PILToTensor (torchvision) vs. ToTensor (albumentations.pytorch)
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4 changes: 2 additions & 2 deletions _posts/papers/computer_vision/2024-01-06-bam.md
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---
layout: post
title: Bottleneck Attention Module (BAM)
title: Bottleneck Attention Module [BAM]
category: Computer Vision
tag: bam
tag: attention
---

# [BAM: Bottleneck Attention Module](https://arxiv.org/abs/1807.06514)
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layout: post
title: Self Attention vs. CNN
category: Computer Vision
tag: self-attention
tag: attention
---

# [ON THE RELATIONSHIP BETWEEN SELF-ATTENTION AND CONVOLUTIONAL LAYERS](https://www.cs.toronto.edu/~bonner/courses/2022s/csc2547/papers/attention/self-attention_and_convolution,_cordonnier,_arxiv2020.pdf)
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layout: post
title: SENet
category: Computer Vision
tag: senet
tag: attention
---

# [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
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layout: post
title: Vision Transformer
category: Computer Vision
tag: vit
tag: transformer
---


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layout: post
title: Bayes Theorem
category: Machine Learning - Theories
category: Deep Learning
tag: bayes
---

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layout: post
title: Classification
category: Machine Learning - Theories
category: Deep Learning
tag: classification
---

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layout: post
title: Information Theory
category: Machine Learning - Theories
category: Deep Learning
tag: information
---

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layout: post
title: Likelihood
category: Machine Learning - Theories
category: Deep Learning
tag: likelihood
---

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layout: post
title: 확률
category: Machine Learning - Theories
category: Deep Learning
tag: probability
---

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