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DINOv2-small 的 ONNX 格式版本，适用于视觉任务
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Rdnet Tiny.nv In1k
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一个轻量级的RDNet图像分类模型，在ImageNet-1k数据集上训练，参数量24M，top-1准确率82.8%。
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Convnextv2 Nano.fcmae
ConvNeXt-V2自监督特征表示模型，采用全卷积掩码自编码器框架(FCMAE)进行预训练，适用于图像分类和特征提取任务。
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基于视觉Transformer(ViT)的自监督图像特征提取模型，采用DINO方法训练
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