Transformer Vision: From Image, Video to World Models & Robotics — Book
This repository is the model-artifact companion to the technical book Transformer Vision: From Image, Video to World Models & Robotics. It keeps the book's trained weights, experiment configuration, evaluation tables, and usage notes together without pretending that every chapter uses one interchangeable model architecture.
Why the repository is organized by chapter
Each chapter asks a different technical question and may use a different architecture, dataset, checkpoint format, preprocessing pipeline, or output type. Chapter-level folders create a stable boundary around those differences:
chapter-NN-short-title/
├── README.md # chapter-specific model card and usage
├── configs/ # resolved experiment configuration
├── checkpoints/ # model, treatment, and seed subfolders
├── results/ # metrics, profiles, and figures
├── EXPERIMENT_REPORT.md # interpreted experiment narrative
└── manifest.json # machine-readable provenance
Inside a chapter, checkpoint folders are further separated by model, training treatment, and seed. This prevents a ViT checkpoint from being confused with a Swin, language, video, world-model, or robotics checkpoint from another part of the book.
Available chapters
| Chapter | Artifact folder | Status |
|---|---|---|
| 4 — Efficient and scalable vision transformers | chapter-04-efficient-and-scalable-vision-transformers |
Complete |
Folders for other chapters will be added only after their experiments and validation are complete. Empty placeholder folders are intentionally avoided.
Download one chapter
Using the Hugging Face CLI:
hf download \
Mayank022/Transformer-Vision-From-Image-Video-to-World-Models-and-Robotics-Book \
--include "chapter-04-efficient-and-scalable-vision-transformers/*" \
--local-dir transformer-vision-book
Readers who need only one seed can download a single file with hf_hub_download; each chapter card
contains exact examples.
File-format policy
Published inference weights use Safetensors. They contain the model state only and exclude optimizer, scheduler, gradient-scaler, and random-number-generator state. This makes the public artifact smaller and avoids pickle execution during weight loading. Full resumable training checkpoints remain in the experiment's controlled storage and are not required for inference.
Every chapter includes SHA-256 checksums and a JSON manifest connecting each exported file to:
- the exact upstream checkpoint identifier;
- architecture and training mode;
- random seed and selected epoch;
- validation and test metrics;
- configuration fingerprint; and
- the checksum of the original training checkpoint.
Important scope note
This is a multi-model book archive, not a single from_pretrained() repository. Always open the
README inside the relevant chapter before loading a file. Model construction and preprocessing are
chapter-specific.
License and attribution
The repository contains fine-tuned derivatives of upstream research checkpoints. For Chapter 4, the official Hugging Face model cards identify five source checkpoints as Apache-2.0 and the Swin-T source checkpoint as MIT. Each chapter lists the exact identifiers and source-license metadata. Retain applicable notices and review the upstream model and dataset terms before making another redistribution or commercial derivative; the book's documentation does not replace those licenses.