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Beit Base Patch16 384.in1k Ft Fungitastic 384

Developed by BVRA
A Danish fungi classification model based on the BEiT architecture, specifically designed for identifying and classifying fungal species.
Downloads 456
Release Time : 6/5/2024

Model Overview

This model is a vision Transformer based on the BEiT architecture, fine-tuned on the Danish Fungi Dataset (DF24), specifically for image classification tasks of fungal species.

Model Features

High-Resolution Processing
Supports high-resolution image input of 384x384 pixels, capable of capturing finer fungal features.
Domain-Specific Optimization
Specifically optimized for fungal classification tasks, demonstrating excellent performance on the Danish Fungi Dataset.
Transformer Architecture
Utilizes the BEiT vision Transformer architecture, which possesses powerful feature extraction capabilities.

Model Capabilities

Fungal Image Classification
Species Identification
Support for Ecological Research

Use Cases

Ecological Research
Field Fungal Surveys
Assists ecologists in quickly identifying fungal samples collected in the field.
Improves species identification efficiency and accuracy.
Citizen Science
Public Fungal Observation
Provides the public with a fungal identification tool to promote citizen science projects.
Expands the scope of biodiversity data collection.
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