# High-Precision Image Classification

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling efficiency through reconstructed Mamba formulas and introducing self-attention modules at the end of the Mamba architecture to improve long-range spatial dependency modeling.

A smart TV gesture recognition model based on the Vision Transformer architecture, capable of accurately classifying 9 common gestures.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, demonstrating excellent performance across multiple visual understanding benchmarks.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, achieving outstanding performance across multiple vision understanding benchmarks.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, demonstrating excellent performance across multiple benchmarks.

AIMv2 is a series of vision models based on multimodal autoregressive objective pretraining, excelling in multiple benchmarks

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, achieving excellent performance across multiple multimodal understanding benchmarks.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, achieving outstanding performance in multiple multimodal understanding benchmarks.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, achieving outstanding performance across multiple visual understanding benchmarks.

AIMv2 is a series of vision models based on multimodal autoregressive objective pretraining, excelling in various vision tasks.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, achieving outstanding performance in multiple benchmarks

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, excelling in various vision tasks.

AIMv2 is a series of vision models pretrained with multimodal autoregressive objectives, excelling in various vision tasks.

An image classification model generated based on HuggingPics, capable of recognizing world-famous landmarks

EfficientNet is a mobile-friendly pure convolutional model that uniformly scales depth, width, and resolution dimensions, trained on the ImageNet-1k dataset.

This is a binary classification model trained with AutoTrain, designed to determine whether objects in images are furry.

This is an image classification model built on PyTorch and HuggingPics, specifically designed for pond scene classification.

CarViT is a car logo classifier based on the Vision Transformer architecture, capable of recognizing logos from 40 different car manufacturers.

This model is designed to identify five prevalent architectural styles from the early to mid-20th century, trained on a database of over 700 images.

This is an image classification model based on the PyTorch framework, capable of accurately distinguishing between cricket and baseball images.

ResNet50 v1.5 is an improved version of the original ResNet50 v1 model, achieving approximately 0.5% higher top1 accuracy by adjusting convolution strides.

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