# High-precision vision model

CoMP-MM-1B is a visual foundation model (VFM) that supports native image resolution input, continuously pre-trained based on SigLIP.

ResNet50x16 visual model based on the CLIP framework, supporting zero-shot image classification tasks

CLIPA-v2 model, focusing on zero-shot image classification tasks, achieving efficient visual representation learning through contrastive image-text training

CLIPA-v2 model, an efficient contrastive vision-language model designed for zero-shot image classification tasks.

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