# Multi-class recognition

Object detection model based on YOLOv9 (GELAN-C) architecture, optimized ONNX version for Transformers.js

YOLOv9-C is an object detection model based on the YOLOv9 architecture, suitable for real-time detection of multiple objects in images.

A speech accent multiclass classification model based on facebook/wav2vec2-base-960h, used to identify different speech accents.

Portuguese zero-shot classification model based on DeBERTa-v3 architecture, suitable for performing text classification tasks without fine-tuning

A pre-trained model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the EuroSAT dataset, suitable for remote sensing image classification tasks.

An image classification model fine-tuned based on Google's ViT-base model, achieving an accuracy of 87.98% on the evaluation set.

This model is an image classification model based on the Google Vision Transformer (ViT) architecture, achieving an accuracy of 86.36% after fine-tuning on the image_folder dataset.