# High-Accuracy Classification

A fine-tuned BERT model for accurately distinguishing between AI-generated and human-written text.

KAT is a novel vision model that replaces the traditional Transformer's channel mixer with Grouped Rational Kolmogorov-Arnold Networks (GR-KAN), trained on the ImageNet-1k dataset.

CvT-21 is a vision transformer model pre-trained on the ImageNet-1k dataset, improving traditional vision transformers by introducing convolutional operations.

RegNet image classification model trained on ImageNet-1k, featuring an efficient network structure designed via neural architecture search

RegNet image classification model trained on ImageNet-1k, designed using neural architecture search technology

RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search

RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search

RegNet is a vision classification model trained on ImageNet-1k, featuring an efficient network structure designed through Neural Architecture Search (NAS)

RegNet model trained on ImageNet-1k, an efficient vision model designed through neural architecture search

RegNet image classification model trained on the ImageNet-1k dataset, designed using neural architecture search technology

RegNet is a vision classification model trained on ImageNet-1k, featuring an efficient network structure designed through neural architecture search

RegNet image classification model trained on the ImageNet-1k dataset, featuring an efficient network structure designed via neural architecture search

RegNet image classification model trained on ImageNet-1k, featuring an efficient network structure designed through neural architecture search

RegNet model trained on ImageNet-1k, an efficient image classification model designed via neural architecture search

RegNet model trained on ImageNet-1k, an efficient vision model designed through Neural Architecture Search (NAS)

ConvNeXT is a pure convolutional model inspired by vision Transformer designs, pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k, outperforming traditional Transformers.

ConvNeXT is a pure convolutional model inspired by vision Transformers, trained on the ImageNet-1k dataset for image classification tasks.

ConvNeXT is a pure convolutional model inspired by the design of vision Transformers, trained on the ImageNet-22k dataset, with performance surpassing Transformers.

ConvNeXT is a pure convolutional model inspired by vision Transformer designs. Trained on the ImageNet-1k dataset at 384x384 resolution, it claims to outperform Transformers in performance.

ROTA is a machine learning model designed to convert free-text crime descriptions into standardized offense code categories, aiding criminal justice research.