3_Vision_Transformer_Models_Categorization.md

Vision Transformer Categorization

Category	Description	Famous Models
Encoder-Only (Image Understanding)	Image classification, detection, feature extraction	ViT, DeiT, Swin, BEiT, ConvNeXt, Twins, Focal Transformer
Decoder-Only (Image Generation)	Autoregressive or masked token image generation	MaskGIT, DALL·E (partially), CogView, CogView2
Encoder-Decoder (Segmentation, Vision-Language, I2I)	Combines vision encoding with decoding for segmentation, captioning, VQA	SETR, Segmenter, Pix2Seq, BLIP, Flamingo, SAM (Segment Anything Model)

Encoder-Only Models (Image Understanding)

Used for image classification, feature extraction, and other vision tasks that require comprehension of visual input.

Model	Description
ViT (Vision Transformer)	The original vision transformer by Google.
DeiT (Data-efficient Image Transformer)	ViT variant by Facebook optimized for smaller datasets.
Swin Transformer	Hierarchical transformer with shifting windows, great for detection and segmentation.
BEiT (BERT Pretraining of Image Transformers)	Inspired by BERT, uses masked image modeling.
ConvNeXt	Combines convolutional ideas with transformer blocks.
Twins	Combines local and global attention.
Focal Transformer	Uses coarse-to-fine attention for better efficiency.

Decoder-Only Models (Image Generation)

Less common, but some models adopt decoder-style structures to autoregressively generate images.

Model	Description
MaskGIT	Generative model using masked token modeling.
DALL·E (partially)	Decoder-style transformer used in autoregressive image generation.
CogView / CogView2	Chinese DALL·E-like models that use decoder transformers for image generation.

Encoder-Decoder Models (Image-to-Image / Vision-Language Tasks)

Used for segmentation, captioning, image translation, and multimodal applications.

Model	Description
SETR (SEgmentation TRansformer)	Applies encoder-decoder architecture for segmentation.
Segmenter	ViT-based model for semantic segmentation.
Pix2Seq / Pix2Seq v2	Treats object detection as a language modeling problem.
BLIP / BLIP-2	Vision-language models for captioning and VQA.
Flamingo	Multimodal model with frozen vision encoder and learnable language decoder.
SAM (Segment Anything Model)	Promptable, Zero-shot, High-resolution capable Segmentation and Object Detection Model. Uses a Vision Transformer (ViT) backbone for image encoding, and a lightweight decoder that processes prompts (points, boxes, masks) to generate segmentation masks.