ConvNeXt Manuscript IIIF Open-Source Image Classification Model - Free Deployment to Assist in Precise Classification of Manuscript Images

Convnext Manuscript Iiif

Developed by davanstrien

An image classification model based on the ConvNeXt architecture, specifically designed for manuscript image classification tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #Manuscript Image Classification #IIIF Standard Adaptation #Few-shot Learning

Downloads 25

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned image classification model based on facebook/convnext-base-224-22k for IIIF manuscript datasets, primarily used for manuscript image classification and recognition

Model Features

Based on ConvNeXt Architecture

Utilizes the advanced ConvNeXt-base architecture with powerful image feature extraction capabilities

Optimized for IIIF Manuscripts

Specifically optimized for IIIF manuscript images, suitable for manuscript classification tasks

Large-scale Pretraining

Based on the facebook/convnext-base-224-22k pretrained model, enriched with extensive visual knowledge

Model Capabilities

Image Classification

Manuscript Recognition

Visual Feature Extraction

Use Cases

Digital Humanities

Manuscript Classification

Classify and recognize digitized manuscript images

F1 score 0.0037 (based on evaluation dataset)

Cultural Heritage Digitization

Ancient Text Recognition

Identify and classify ancient literature images

🚀 convnext_manuscript_iiif

This model is a fine - tuned version of [facebook/convnext - base - 224 - 22k](https://huggingface.co/facebook/convnext - base - 224 - 22k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset, achieving certain results in image classification.

🚀 Quick Start

This model is a fine - tuned version of [facebook/convnext - base - 224 - 22k](https://huggingface.co/facebook/convnext - base - 224 - 22k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set:

Loss: 5.5856
F1: 0.0037

📚 Documentation

Training and Evaluation

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 64
eval_batch_size: 64
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 30.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
6.5753	1.0	2038	6.4121	0.0016
5.9865	2.0	4076	5.9466	0.0021
5.6521	3.0	6114	5.7645	0.0029
5.3123	4.0	8152	5.6890	0.0033
5.0337	5.0	10190	5.6692	0.0034
4.743	6.0	12228	5.5856	0.0037
4.4387	7.0	14266	5.5969	0.0042
4.1422	8.0	16304	5.6711	0.0043
3.8372	9.0	18342	5.6761	0.0044
3.5244	10.0	20380	5.8469	0.0042
3.2321	11.0	22418	5.8774	0.0045
2.9004	12.0	24456	6.1186	0.0047
2.5937	13.0	26494	6.2398	0.0046
2.2983	14.0	28532	6.3732	0.0049
2.0611	15.0	30570	6.5024	0.0045
1.8153	16.0	32608	6.6585	0.0047
1.6075	17.0	34646	6.8333	0.0043
1.4342	18.0	36684	6.9529	0.0044
1.2614	19.0	38722	7.1129	0.0046
1.1463	20.0	40760	7.1977	0.0039
1.0387	21.0	42798	7.2700	0.0044
0.9635	22.0	44836	7.3375	0.0040
0.8872	23.0	46874	7.4003	0.0039
0.8156	24.0	48912	7.4884	0.0039
0.7544	25.0	50950	7.4764	0.0039
0.6893	26.0	52988	7.5153	0.0042
0.6767	27.0	55026	7.5427	0.0043
0.6098	28.0	57064	7.5547	0.0042
0.5871	29.0	59102	7.5533	0.0041
0.5696	30.0	61140	7.5595	0.0041

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.11.6

📄 License

This model is licensed under the Apache - 2.0 license.

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