Transformers in Action: From AST to YOLOS — A Hands-On Tutorial Collection
If you’ve been following the transformer world, you know it’s not just about NLP anymore. Vision transformers, audio transformers, multimodal models — they’re everywhere. But reading papers can only take you so far. At some point, you want to actually run the code, tweak it, and see what happens.
That’s exactly what this collection of tutorials is for. No abstract theory, no endless equations. Just practical Jupyter notebooks that get you from zero to running real transformer models — from AST (Audio Spectrogram Transformer) all the way to YOLOS (You Only Look at One Sequence).
What It Does
The repository is a curated set of hands-on tutorials, each focused on a specific transformer model or use case. You get self-contained notebooks that walk you through loading a pretrained model, preparing data, running inference, and often visualizing results. Think of it as a cookbook, not a textbook.
The models covered include:
- Audio transformers (AST)
- Vision transformers (ViT, DETR, YOLOS)
- Multimodal models (CLIP, BLIP, Flava)
- Time series transformers (PatchTST, TimesNet)
- And more — like Table Transformer, DiT, and GIT
Each notebook is designed to be run in a free cloud environment (like Google Colab) or locally with minimal setup.
Why It’s Cool
What makes this collection stand out is how it bridges the gap between “I read the paper” and “I can actually use this.” The author, Niels Rogge, has a knack for stripping away fluff and leaving you with the core logic. The notebooks are clean, well commented, and usually require just a few lines of code to get a model running.
Some highlights:
- AST notebook lets you classify audio clips with a transformer — something most devs haven’t tried yet.
- YOLOS shows you object detection without anchor boxes or convolutional backbones, just a plain ViT.
- DETR notebooks demo end-to-end object detection with transformers, no NMS needed.
If you’re experimenting with transformers for a side project or just want to understand how they work under the hood, these tutorials save you hours of debugging broken imports or outdated APIs.
How to Try It
You don’t need to install anything heavy to get started. Each notebook has a “Open in Colab” button, so you can run everything in your browser with a free GPU.
- Go to the repository: Transformers-Tutorials
- Pick a model that sounds interesting
- Click the notebook, then click the Colab badge at the top
- Run the cells one by one — that’s it
If you prefer local setup, the requirements are standard: PyTorch, Hugging Face Transformers, and a few model-specific extras like torchaudio or torchvision.
Final Thoughts
This collection is a solid resource if you’re tired of reading about transformers and actually want to use them. It’s not trying to sell you on the hype — it just gives you working code. I’ve personally used the AST notebook to experiment with environmental sound classification, and the DETR notebook for a quick prototype on object detection in drone footage.
If you’re a developer who learns best by tinkering, this is a great place to start. And if you’re curious about vision or audio tasks, you’ll find something here that surprises you.
Found via @githubprojects