detr resnet 50 panoptic

Introduction

DETR (DEtection TRansformer) with ResNet-50 is an end-to-end object detection model designed for panoptic segmentation on the COCO 2017 dataset. It was introduced by Carion et al. in the paper "End-to-End Object Detection with Transformers." This model uses a transformer architecture for direct set prediction, bypassing the need for many traditional object detection components.

Architecture

The DETR model features an encoder-decoder transformer architecture with a ResNet-50 convolutional backbone. It includes two heads on the decoder outputs: a linear layer for classifying detected objects and an MLP for predicting bounding boxes. The model employs object queries that correspond to potential objects in an image, with the COCO dataset using 100 object queries. The training process involves bipartite matching loss and the Hungarian matching algorithm to optimize object prediction accuracy.

Training

The model was trained on the COCO 2017 dataset, which contains 118,000 images for training. Preprocessing includes resizing images to a minimum of 800 pixels on the shortest side and a maximum of 1333 pixels on the longest side, with normalization using ImageNet statistics. The training spanned 300 epochs using 16 V100 GPUs, with a batch size of 64, taking approximately three days.

Guide: Running Locally

To run DETR locally, follow these steps:

1. Install Required Libraries: Ensure you have PyTorch and the Hugging Face Transformers library installed.
2. Load Image and Model: Use the DetrFeatureExtractor and DetrForSegmentation from the Transformers library.
3. Prepare the Image: Resize and normalize the image using the feature extractor.
4. Run Inference: Pass the processed image through the model to obtain segmentation results.
5. Post-process: Use the post_process_panoptic method to convert outputs to COCO format.

For enhanced performance, consider using a cloud GPU service like AWS EC2 with NVIDIA GPU instances.

License

The DETR model is released under the Apache 2.0 license, allowing for broad usage and modification.