Large - algae - vit - rgb Open - source Visual Model: Freely Achieve Precise Classification of Algae Images

Large Algae Vit Rgb

Developed by samitizerxu

This model is a vision model based on the Vision Transformer (ViT) architecture, focusing on the classification task of algae images.

Image Classification

Transformers

Open Source License:Apache-2.0 #Algae Identification #RGB Image Processing #ViT Architecture

Downloads 39

Release Time : 2/16/2023

Model Overview

large-algae-vit-rgb is a vision model for algae image classification, based on the ViT architecture and fine-tuned on a specific dataset.

Model Features

Based on ViT Architecture

Utilizes the Vision Transformer architecture, suitable for processing image data

Algae Image Classification

Specifically designed for classification tasks of algae images

Moderate Accuracy

Achieves approximately 58% accuracy on the evaluation set

Model Capabilities

Image Classification

Algae Identification

Use Cases

Environmental Monitoring

Algae Bloom Monitoring

Used to monitor algae growth in water bodies

Biological Research

Algae Species Identification

Identify different species of algae

🚀 large-algae-vit-rgb

This model is a fine - tuned version of samitizerxu/large-algae-vit-rgb on the None dataset. It provides evaluation results on the loss and accuracy metrics, offering valuable insights for relevant tasks.

🚀 Quick Start

This model is a fine-tuned version of samitizerxu/large-algae-vit-rgb on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.1659
Accuracy: 0.5798

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2115	1.0	120	0.9078	0.6315
1.1249	2.0	240	0.9217	0.6320
1.1385	3.0	360	0.9518	0.6180
1.1347	4.0	480	1.0201	0.6068
1.1358	5.0	600	1.0801	0.5892
1.098	6.0	720	1.0932	0.5851
1.0882	7.0	840	1.0347	0.6033
1.0688	8.0	960	1.0403	0.6056
1.0863	9.0	1080	1.0466	0.6009
1.1253	10.0	1200	1.2308	0.5511
1.0393	11.0	1320	1.1434	0.5869
1.0749	12.0	1440	1.2155	0.5622
1.0433	13.0	1560	1.2466	0.5522
1.0141	14.0	1680	1.1880	0.5563
1.0516	15.0	1800	1.1006	0.5992
1.0696	16.0	1920	1.0971	0.5751
0.9867	17.0	2040	1.1689	0.5827
1.0234	18.0	2160	1.1846	0.5751
1.0364	19.0	2280	1.1480	0.5739
1.0314	20.0	2400	1.0977	0.5880
1.0179	21.0	2520	1.1258	0.5851
1.0584	22.0	2640	1.1569	0.5822
1.0222	23.0	2760	1.1672	0.5839
0.996	24.0	2880	1.1737	0.5798
1.0343	25.0	3000	1.1588	0.5792
0.9854	26.0	3120	1.1758	0.5763
0.9753	27.0	3240	1.1715	0.5763
0.9881	28.0	3360	1.1403	0.5839
1.0057	29.0	3480	1.1765	0.5781
0.9824	30.0	3600	1.1659	0.5798

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

📄 License

This model is licensed under the Apache-2.0 license.

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