Model Card for mr_tf_efficientnet_b3_ns_timm_pretrain_railspace_and_building

A EfficientNet image classification model. Trained on ImageNet-1k and unlabeled JFT-300m using Noisy Student semi-supervised learning in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. Fine-tuned on gold standard annotations and outputs from early experiments using MapReader (found here).

Model Details

Model Description

Model type: Image classification /feature backbone
Finetuned from model: https://huggingface.co/timm/tf_efficientnet_b3.ns_jft_in1k

Classes and labels

0: no
1: railspace
2: building
3: railspace & building

Uses

This fine-tuned version of the model is an output of the MapReader pipeline. It was used to classify 'patch' images (cells/regions) of scanned nineteenth-century series maps of Britain provided by the National Library of Scotland (learn more here). We classified patches to indicate the presence of buildings and railway infrastructure. See our paper for more details about labels.

How to Get Started with the Model in MapReader

Please go to the MapReader documentation for instructions on how to use this model in MapReader.

Training, Evaluation and Testing Details

Training, Evaluation and Testing Data

This model was fine-tuned on manually-annotated data.

Training, Evaluation and Testing Procedure

Details can be found here.

Open access version of the article available here.

Results

Data outputs can be found here.

Further details can be found here.

More Information

This model was fine-tuned using MapReader.

The code for MapReader can be found here and the documentation can be found here.

Model Card Authors

Katie McDonough (k.mcdonough@lancaster.ac.uk)

Rosie Wood (rwood@turing.ac.uk)

Model Card Contact