Category: Computer Vision
-
ONNX Runtime๊ณผ TensorRT๋ก ์ปดํจํฐ๋น์ ๋ชจ๋ธ ๊ฒฝ๋ํํ๊ธฐ: Edge AI ๋ฐฐํฌ ์ค์ ๊ฐ์ด๋
Speed up YOLO inference 8x on Jetson with TensorRT INT8 quantization. ONNX conversion pitfalls and FP16 vs INT8 accuracy tradeoffs included.
-
ConvNeXt vs Swin Transformer: ํ์ด๋ธ๋ฆฌ๋ ์ํคํ ์ฒ ์ค์ ์ฑ๋ฅ ๋น๊ต์ ์ต์ ์ ํ ์ ๋ต
ConvNeXt vs Swin Transformer on ImageNet, COCO, ADE20K: real accuracy and speed numbers to pick the right architecture for your project.
-
SAM๊ณผ DINOv2๋ก ๋ผ๋ฒจ ์์ด ์์ํ๋ Zero-Shot Semantic Segmentation ์ค์ ๊ฐ์ด๋
SAM + DINOv2 semantic segmentation with zero labeled images: 81% mIoU on custom industrial datasets. Step-by-step implementation guide.
-
YOLO v8๋ถํฐ v11๊น์ง ์ฑ๋ฅ ๋ฒค์น๋งํฌ: ์ค์๊ฐ ๊ฐ์ฒด ํ์ง ๋ชจ๋ธ ์ ํ ๊ฐ์ด๋
YOLOv8 vs v11: real-world FPS and mAP benchmarks on edge devices. See which version actually wins for your hardware.
-
ViT vs CNN Attention Map ๋น๊ต: ๋ชจ๋ธ ํด์ 5๊ฐ์ง ๊ธฐ๋ฒ
ViT vs CNN attention maps decoded: 5 techniques to visualize what your model actually sees. GradCAM, rollout, and flow compared.
-
ViT vs ConvNeXt: 2026๋ ์ด๋ฏธ์ง ๋ถ๋ฅ ๋ชจ๋ธ ์ํคํ ์ฒ ์ ํ ๊ฐ์ด๋
ViT or ConvNeXt for image classification in 2026? Speed vs accuracy tradeoffs tested on ImageNet with deployment recommendations.