2nd/10_Wiki/Topic_Programming/From_Topics_Root/CNN.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-cnn	CNN (Convolutional Neural Network)	10_Wiki/Topics	verified	self	ConvNet Convolutional Network	none	A	0.95	applied	deep-learning computer-vision cnn neural-network		2026-05-10	pending	language framework Python PyTorch 2.5 / JAX

CNN (Convolutional Neural Network)

매 한 줄

"매 CNN 의 핵심: spatial locality + parameter sharing + translation equivariance". 매 1989 LeCun LeNet 으로 시작, 매 2012 AlexNet 의 ImageNet breakthrough 가 deep-learning era 의 trigger. 매 2026 현재 ViT 의 주류 진입 unauthenticated, ConvNeXt-V2 / EfficientNet-V2 / RegNet 같은 modern CNN 의 efficiency 의 강점, 매 mobile / edge 의 dominant.

매 핵심

매 architectural primitive

• Conv2d: 매 sliding kernel — 매 (in_ch, out_ch, kH, kW) parameters.
• Pooling: max/avg — 매 spatial downsampling.
• BatchNorm / GroupNorm: 매 internal covariate shift mitigation.
• Residual connection (ResNet): 매 identity skip — 매 vanishing gradient solved.
• Depthwise-separable conv (MobileNet): 매 efficient — 매 9× FLOPs 감소.

매 inductive biases

• Locality: 매 nearby pixels correlated.
• Translation equivariance: 매 object 의 위치 shift 도 같은 feature.
• Hierarchy: 매 edge → texture → part → object.

매 응용

1. Image classification (ResNet, ConvNeXt, EfficientNet).
2. Object detection (YOLO v11, RT-DETR backbone).
3. Segmentation (U-Net, DeepLab v3+).
4. Audio spectrograms, time-series, medical imaging.

💻 패턴

Basic CNN block (PyTorch)

import torch.nn as nn

class ConvBlock(nn.Module):
    def __init__(self, in_c, out_c, k=3, s=1):
        super().__init__()
        self.conv = nn.Conv2d(in_c, out_c, k, s, padding=k//2, bias=False)
        self.bn = nn.BatchNorm2d(out_c)
        self.act = nn.GELU()
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

Residual block (ResNet-style)

class ResBlock(nn.Module):
    def __init__(self, c):
        super().__init__()
        self.b1 = ConvBlock(c, c)
        self.b2 = ConvBlock(c, c)
    def forward(self, x):
        return x + self.b2(self.b1(x))

Depthwise-separable (MobileNet)

class DWSep(nn.Module):
    def __init__(self, in_c, out_c, s=1):
        super().__init__()
        self.dw = nn.Conv2d(in_c, in_c, 3, s, 1, groups=in_c, bias=False)
        self.pw = nn.Conv2d(in_c, out_c, 1, 1, 0, bias=False)
        self.bn = nn.BatchNorm2d(out_c)
        self.act = nn.GELU()
    def forward(self, x):
        return self.act(self.bn(self.pw(self.dw(x))))

ConvNeXt block (2026 modern CNN)

class ConvNeXtBlock(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, 7, padding=3, groups=dim)
        self.norm = nn.LayerNorm(dim)
        self.pw1 = nn.Linear(dim, 4 * dim)
        self.act = nn.GELU()
        self.pw2 = nn.Linear(4 * dim, dim)
    def forward(self, x):
        i = x
        x = self.dwconv(x).permute(0, 2, 3, 1)  # NCHW -> NHWC
        x = self.pw2(self.act(self.pw1(self.norm(x))))
        return i + x.permute(0, 3, 1, 2)

Training loop with mixed precision

import torch
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
opt = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=0.05)
for x, y in loader:
    opt.zero_grad()
    with autocast():
        loss = nn.functional.cross_entropy(model(x.cuda()), y.cuda())
    scaler.scale(loss).backward()
    scaler.step(opt)
    scaler.update()

Inference with TorchScript / compile

model.eval()
model = torch.compile(model, mode="reduce-overhead")  # PyTorch 2.5+
with torch.no_grad():
    out = model(x)

매 결정 기준

상황	Approach
Small data (<10k images)	Pretrained ResNet-50 + finetune
Mobile / edge	MobileNetV4 / EfficientNet-Lite
SOTA on ImageNet	ConvNeXt-V2 or hybrid (CNN+ViT)
Real-time detection	YOLOv11 (CSPDarknet backbone)
Medical seg	U-Net++ or nnU-Net

기본값: 매 timm 의 pretrained ConvNeXt-Tiny — 매 81%+ ImageNet, 매 28M params.

🔗 Graph

• 부모: Deep Learning · Neural Networks
• 변형: ResNet · EfficientNet
• 응용: Computer Vision · Object Detection · Image Segmentation
• Adjacent: Transformer_Architecture_and_LLM_Foundations

🤖 LLM 활용

언제: 매 architecture sketch 의 generation, 매 training-loop boilerplate, 매 hyperparameter starting points, 매 debugging shape mismatches. 언제 X: 매 SOTA tuning / benchmark 의 LLM 의존 X — 매 paper + timm 의 reference.

❌ 안티패턴

• Vanilla VGG-style 의 2026 사용: 매 outdated — 매 ResNet/ConvNeXt 의 사용.
• No data augmentation: 매 immediate overfit on small data.
• BatchNorm with batch size 1: 매 statistic 무의미 — 매 GroupNorm 사용.
• Conv 후 immediate ReLU + BN order 의 inconsistent: 매 BN→Act 의 standard.
• No mixed precision on modern GPU: 매 free 2× speedup 의 손실.

🧪 검증 / 중복

• Verified (LeCun 1989, He et al. 2015 ResNet, Liu et al. 2022 ConvNeXt, 2024 ConvNeXt-V2).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — CNN fundamentals + ConvNeXt modern patterns