vit-Diatome Open-source Visual Classification Model - Fine-tuned Based on ViT with a Classification Accuracy of 94.29%

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Vit Diatome

Developed by sgonzalezsilot

A vision classification model fine-tuned on the Diatome dataset based on Google's ViT model, achieving 94.29% accuracy

Image Classification

Transformers

Open Source License:Apache-2.0 #Diatom identification #High accuracy #Image classification

Downloads 38

Release Time : 3/19/2023

Model Overview

This model is a fine-tuned version of Google's ViT-base-patch16-224-in21k model on the Diatome dataset, primarily used for image classification tasks.

Model Features

High accuracy

Achieves 94.29% accuracy on the evaluation set

Based on ViT architecture

Utilizes the Vision Transformer architecture with powerful image feature extraction capabilities

Efficient fine-tuning

Achieves good results with just 4 rounds of fine-tuning on the base model

Model Capabilities

Image classification

Feature extraction

Use Cases

Biological image analysis

Diatom classification

Used to identify and classify different types of diatoms

Accuracy reaches 94.29%

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2009	0.23	100	1.9938	0.6045
1.2823	0.47	200	1.2293	0.7327
0.7569	0.7	300	0.9534	0.7868
0.7428	0.93	400	0.7906	0.8078
0.4309	1.16	500	0.5759	0.8538
0.349	1.4	600	0.5070	0.8742
0.517	1.63	700	0.5048	0.8794
0.3667	1.86	800	0.5212	0.8596
0.169	2.09	900	0.4112	0.8888
0.1443	2.33	1000	0.3294	0.9109
0.1389	2.56	1100	0.3146	0.9190
0.142	2.79	1200	0.2994	0.9208
0.0921	3.02	1300	0.2620	0.9324
0.0768	3.26	1400	0.2516	0.9336
0.061	3.49	1500	0.2425	0.9388
0.0729	3.72	1600	0.2335	0.9418
0.0757	3.95	1700	0.2285	0.9429

Property	Details
Model Type	vit - Diatome
Training Data	Diatome dataset
Metrics	accuracy

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Vit Diatome

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