Detection Transformer (DETR) for Object Detection

code: https://github.com/unionai/workshops ​Object detection is still one of the highest-ROI models in applied ML, powering quality inspection, inventory systems, safety monitoring, medical imaging, and autonomous systems. Fine-tuned detectors consistently outperform general-purpose vision models on domain-specific tasks, and they do it with a fraction of the compute, latency, and cost. ​In this hands-on workshop, we'll fine-tune a DETR (DEtection TRansformer) model on a custom dataset and deploy it behind a simple UI. By the end, you'll have a working detector, a walkthrough of how to build your own datasets, and a reusable pipeline you can point at your next detection problem. A full end-to-end computer vision workflow built on infrastructure that scales from your laptop to a production cluster. ​What we'll cover ​A practical intro to DETR and why transformers changed object detection ​How to build your own dataset: collection, labeling workflows, and common pitfalls ​Fine-tuning DETR with Hugging Face Transformers ​Orchestrating the pipeline with Flyte 2: cached data prep, GPU-aware training, and reproducible runs ​Deploying the model with aUI, with a path to scaled inference ​Patterns for extending to your own detection problem ​What you'll leave with ​A fine-tuned DETR model trained on a custom dataset ​A reusable training and deployment pipeline you can adapt to your own data ​The knowledge to build and label your own datasets for future projects ​A portfolio-ready project and a certificate of participation ​Who it's for ​ML engineers and practitioners who want to move past pretrained demos and train detectors on their own data. Whether you're prototyping at work, evaluating infrastructure for a production CV use case, or building a portfolio project, you'll leave with code you can keep extending. ​Hosted by Sage Elliott, AI Engineer at Union.ai.