# High-Resolution Image Processing

DeepEyes is a vision-language model that encourages 'thinking with images' through reinforcement learning. It can directly integrate visual information into the reasoning chain and performs excellently in image-text processing tasks.

UPerNet semantic segmentation model based on Swin Transformer architecture, suitable for high-precision image segmentation tasks

UPerNet semantic segmentation model based on Swin Transformer small architecture, suitable for scene parsing tasks like ADE20K

UPerNet is a semantic segmentation model based on the ConvNeXt-Tiny architecture, suitable for image segmentation tasks.

SegFormer model optimized for coral reef semantic segmentation tasks, fine-tuned on the Coralscapes dataset at 1024x1024 resolution

This is a semantic segmentation model based on the SegFormer architecture, specifically optimized for coral reef ecosystem image segmentation tasks and fine-tuned on the Coralscapes dataset.

A vision-language model based on the SigLIP image encoder, utilizing global average pooling, suitable for multimodal tasks.

A semantic segmentation model based on the Segformer architecture, optimized for the Cityscapes dataset

A lightweight semantic segmentation model based on Segformer architecture, pre-trained on the Cityscapes dataset

A semantic segmentation model based on the Segformer architecture, specifically optimized for the Cityscapes dataset

PyTorch-based Segformer model for semantic segmentation tasks, pre-trained on the ADE20K dataset

mPLUG-Owl3 is an advanced multimodal large language model that focuses on solving the problem of long image sequence understanding. It significantly improves the processing speed and sequence length support through the hyper attention mechanism.

A Danish fungi classification model based on the BEiT architecture, specifically designed for identifying and classifying fungal species.

LLaVA-JP is a multimodal vision-language model that supports Japanese, capable of understanding and generating descriptions and dialogues about input images.

ViTamin-XL-256px is a vision-language model based on the ViTamin architecture, designed for efficient visual feature extraction and multimodal tasks, supporting high-resolution image processing.

ViTamin-XL-384px is a large-scale vision-language model based on the ViTamin architecture, specifically designed for vision-language tasks, supporting high-resolution image processing and multimodal feature extraction.

SigLIP-based vision model that enhances maximum resolution to 980x980 through interpolated positional embeddings and implements NaViT strategy for variable resolution and aspect ratio-preserving image processing

An image editing model fine-tuned on Stable Diffusion XL (SDXL) using the InstructPix2Pix method, supporting image editing through natural language instructions.

SegFormer is a Transformer-based semantic segmentation model fine-tuned on the ADE20k dataset, suitable for image segmentation tasks.