In this tutorial, we’ll show you how to train Object Detection Transformers using DETR as an example. We’ll guide you through every step of the process, starting with setting up your Python environment and demonstrating DETR model inference on example images.

Next, we’ll show you how to download custom datasets from Roboflow Universe and build custom PyTorch COCO Detection datasets. We’ll also cover how to visualize COCO dataset entries and build custom PyTorch data loaders.

At the core of this tutorial, we’ll demonstrate how to build a custom PyTorch Lightning DETR module for training your own object detection model. We’ll then guide you through training DETR on your custom dataset, followed by custom DETR model inference and evaluation.

Chapters:

0:00 Introduction
1:06 Setting up the Python environment
3:19 DETR model inference on example images
8:40 Download custom dataset from Roboflow Universe
10:31 Building custom PyTorch COCO Detection datasets
12:25 Visualising COCO datasets entry
13:15 Building custom PyTorch Data Loaders
15:59 Building custom PyTorch Lightning DETR Module
18:50 Training DETR on custom dataset
20:06 Custom DETR model inference
22:07 Evaluating custom DETR model
22:40 Outro

Resources:

🌏 Roboflow: https://roboflow.com
🌌 Roboflow Universe: https://universe.roboflow.com

🗒️ How to Train DETR with 🤗 Transformers on a Custom Dataset notebook: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-huggingface-detr-on-custom-dataset.ipynb
🤗 Transformers DETR documentation: https://huggingface.co/docs/transformers/model_doc/detr
🎬 Exploring The COCO Dataset YouTube video: https://youtu.be/B4gNml3V2cc
⌛ PyTorch Data Loader documentation: https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
⚡PyTorch Lightning Module documentation: https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html

Stay updated with the projects I’m working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐

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