2nd/10_Wiki/Topics/AI_and_ML/Image-Classification-Mastery.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-image-classification-mastery	Image Classification	10_Wiki/Topics	verified	self	image classification ResNet ViT EfficientNet ImageNet CLIP	none	A	0.96	applied	computer-vision classification resnet vit efficientnet clip imagenet		2026-05-10	pending	language framework Python PyTorch / timm / Transformers

Image Classification

매 한 줄

"매 image → class label". 매 ImageNet benchmark. 매 evolution: AlexNet 2012 → VGG → ResNet 2015 → EfficientNet → ViT 2020 → CLIP / DINOv2. 매 modern: 매 foundation model 의 zero-shot.

매 핵심

매 architecture evolution

• AlexNet (2012): 매 deep learning revival.
• VGG (2014): 매 deeper.
• ResNet (2015): 매 skip connection.
• EfficientNet (2019): 매 compound scaling.
• ViT (2020): 매 transformer.
• ConvNeXt (2022): 매 modern CNN.
• DINOv2 (2023): 매 self-supervised.
• CLIP (2021): 매 zero-shot.

매 응용

1. Medical (X-ray, pathology).
2. Industrial (defect detection).
3. Retail (visual search).
4. Wildlife (camera trap).
5. Content moderation.

💻 패턴

timm (modern model zoo)

import timm
model = timm.create_model('vit_base_patch16_224', pretrained=True)
data_config = timm.data.resolve_data_config({}, model=model)
transforms = timm.data.create_transform(**data_config)

Fine-tune (PyTorch)

import torch
from torchvision.models import resnet50, ResNet50_Weights
model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
model.fc = torch.nn.Linear(2048, n_classes)

# 매 freeze backbone (transfer learning baseline)
for p in model.parameters(): p.requires_grad = False
for p in model.fc.parameters(): p.requires_grad = True

optim = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)

CLIP zero-shot

from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained('openai/clip-vit-large-patch14')
processor = CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14')

texts = ['a photo of a dog', 'a photo of a cat', 'a photo of a bird']
inputs = processor(text=texts, images=image, return_tensors='pt')
out = model(**inputs)
probs = out.logits_per_image.softmax(dim=-1)

DINOv2 (self-supervised features)

import torch
dinov2 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
features = dinov2(image)  # 매 frozen embedding for downstream

Mixup

def mixup(x, y, alpha=0.4):
    lam = np.random.beta(alpha, alpha)
    idx = torch.randperm(x.size(0))
    return lam * x + (1 - lam) * x[idx], y, y[idx], lam

CutMix

def cutmix(x, y, alpha=1.0):
    lam = np.random.beta(alpha, alpha)
    H, W = x.shape[-2:]
    cut_w = int(W * (1 - lam) ** 0.5)
    cut_h = int(H * (1 - lam) ** 0.5)
    cx, cy = np.random.randint(W), np.random.randint(H)
    x1, y1 = max(0, cx - cut_w//2), max(0, cy - cut_h//2)
    x2, y2 = min(W, cx + cut_w//2), min(H, cy + cut_h//2)
    idx = torch.randperm(x.size(0))
    x[:, :, y1:y2, x1:x2] = x[idx, :, y1:y2, x1:x2]
    return x, y, y[idx], 1 - (x2-x1)*(y2-y1)/(W*H)

Augmentation (albumentations)

import albumentations as A
augment = A.Compose([
    A.RandomResizedCrop(224, 224),
    A.HorizontalFlip(),
    A.ColorJitter(0.2, 0.2, 0.2, 0.1),
    A.RandomErasing(p=0.25),
    A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

Test-Time Augmentation

def tta_predict(model, image, n_aug=5):
    predictions = []
    for _ in range(n_aug):
        aug_img = random_augment(image)
        predictions.append(model(aug_img).softmax(-1))
    return torch.stack(predictions).mean(0)

Top-K accuracy

def topk_accuracy(logits, labels, k=5):
    topk = logits.topk(k, dim=-1).indices
    correct = (topk == labels.unsqueeze(-1)).any(dim=-1).float().mean()
    return correct

Model card (best practice)

model: my-classifier-v2
backbone: vit_base_patch16_224
training_data: ImageNet-1k + custom 100k
classes: 1000
augmentation: RandAugment + Mixup + CutMix
ttest_top1: 84.5
test_top5: 97.2
calibration_ece: 0.034
inference_ms_a100: 8

Modern recipe (DeiT, ViT)

def modern_train_recipe():
    return {
        'optimizer': 'AdamW',
        'lr': 1e-3, 'wd': 0.05,
        'scheduler': 'cosine + warmup 5 epochs',
        'epochs': 300,
        'augmentation': 'RandAugment + Mixup 0.8 + CutMix 1.0',
        'label_smoothing': 0.1,
        'stochastic_depth': 0.1,
        'ema': True,
    }

매 결정 기준

상황	Model
Need pretrained	timm
Best ImageNet	DeiT III / ViT-L
Mobile	MobileNetV3 / EfficientNet-Lite
Zero-shot	CLIP
Self-supervised	DINOv2
Tiny	ResNet18 / EfficientNet-B0

기본값: 매 timm + 매 ViT-B/L pretrained + 매 modern recipe (RandAug + Mixup + CutMix + label smooth) + 매 TTA 의 critical eval.

🔗 Graph

• 부모: Computer Vision
• 변형: ResNet · ViT · EfficientNet
• 응용: CLIP · Image-Segmentation
• Adjacent: Foundation-Models

🤖 LLM 활용

언제: 매 image task. 매 visual search. 매 medical. 언제 X: 매 segmentation / detection (다른 task).

❌ 안티패턴

• Train from scratch: 매 timm pretrained 의 use.
• No augment: 매 overfit.
• Top-1 only: 매 also top-5 / calibration.
• No TTA at eval: 매 lose 1-2%.

🧪 검증 / 중복

• Verified (timm, He ResNet 2015, Dosovitskiy ViT 2020, Oquab DINOv2 2023).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — 매 evolution + timm / CLIP / DINOv2 / Mixup / TTA code