---
'[object Object]': null
language:
- en
library_name: timm
pipeline_tag: image-classification
tags:
- vision
- mapreader
- maps
- National Library of Scotland
- historical
- lam
- humanities
- heritage
---

# Model Card for mr_tf_efficientnet_b3_ns_timm_pretrain

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](https://huggingface.co/datasets/Livingwithmachines/MapReader_Data_SIGSPATIAL_2022)).

## Model Details

### Model Description

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

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## How to Get Started with the Model

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## Training Details

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#### Training Hyperparameters

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## Evaluation

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## Citation [optional]

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## Funding Statement

This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1). 
Living with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and Cambridge, King's College London, East Anglia, Exeter, and Queen Mary University of London.