{
  "models": [
    {
      "name": "Clone-detection-BigCloneBench",
      "tags": {
        "data_type": ["code"],
        "task_type": ["classification", "similarity"],
        "domain": ["code_clone_detection"],
        "input_type": "code_pair",
        "output_type": "binary"
      },
      "original_path": "Code-Code/Clone-detection-BigCloneBench",
      "description": "基于大规模代码克隆基准数据集的代码克隆检测模型，任务是进行二元分类(0/1),其中1代表语义等价，0代表其他情况。",
      "dataset": "BigCloneBench数据集",
      "epoch": "待上传"
    },
    {
      "name": "Clone-detection-POJ-104",
      "tags": {
        "data_type": ["code"],
        "task_type": ["retrieval", "similarity"],
        "domain": ["code_clone_detection"],
        "input_type": "code",
        "output_type": "code_ranking"
      },
      "original_path": "Code-Code/Clone-detection-POJ-104",
      "description": "基于POJ-104数据集的代码克隆检测模型，任务是识别不同编程题目中相似的代码实现，给定一段代码和一组候选代码，任务是返回具有相同语义的Top K个代码",
      "dataset": "POJ-104编程题目数据集",
      "epoch": "待上传"
    },
    {
      "name": "CodeCompletion-token",
      "tags": {
        "data_type": ["code"],
        "task_type": ["generation", "completion"],
        "domain": ["code_completion"],
        "input_type": "code_tokens",
        "output_type": "code_tokens"
      },
      "original_path": "Code-Code/CodeCompletion-token",
      "description": "基于token级别的代码自动补全模型",
      "dataset": "Java代码token序列数据集",
      "epoch": "待上传"
    },
    {
      "name": "Defect-detection",
      "tags": {
        "data_type": ["code"],
        "task_type": ["classification"],
        "domain": ["code_defect_detection"],
        "input_type": "code",
        "output_type": "binary"
      },
      "original_path": "Code-Code/Defect-detection",
      "description": "代码缺陷检测模型，通过分析代码来识别潜在的缺陷和错误（进行二元分类(0/1)）",
      "dataset": "包含缺陷标注的C语言代码数据集",
      "epoch": "待上传"
    },
    {
      "name": "code-refinement",
      "tags": {
        "data_type": ["code"],
        "task_type": ["generation", "optimization"],
        "domain": ["code_optimization"],
        "input_type": "code",
        "output_type": "code"
      },
      "original_path": "Code-Code/code-refinement",
      "description": "代码优化模型",
      "dataset": "代码优化前后对数据集（C语言）",
      "epoch": "待上传"
    },
    {
      "name": "code-to-text",
      "tags": {
        "data_type": ["code", "text"],
        "task_type": ["generation", "translation"],
        "domain": ["code_documentation"],
        "input_type": "code",
        "output_type": "text"
      },
      "original_path": "Code-Text/code-to-text",
      "description": "代码到自然语言的转换模型",
      "dataset": "多语言代码-文本对数据集",
      "epoch": "待上传"
    },
    {
      "name": "NL-code-search-Adv",
      "tags": {
        "data_type": ["code", "text"],
        "task_type": ["retrieval", "search"],
        "domain": ["code_search"],
        "input_type": "text",
        "output_type": "code"
      },
      "original_path": "Text-code/NL-code-search-Adv",
      "description": "高级自然语言代码搜索模型，通过计算自然语言查询与代码片段之间的相似性来实现代码搜索",
      "dataset": "自然语言-(python)代码对数据集",
      "epoch": "待上传"
    },
    {
      "name": "NL-code-search-WebQuery",
      "tags": {
        "data_type": ["code", "text"],
        "task_type": ["retrieval", "search"],
        "domain": ["code_search"],
        "input_type": "text",
        "output_type": "code"
      },
      "original_path": "Text-code/NL-code-search-WebQuery",
      "description": "基于Web查询的代码搜索模型，该模型通过编码器处理代码和自然语言输入，并利用多层感知器（MLP）来计算相似性得分",
      "dataset": "Web查询-代码对数据集（CodeSearchNet数据集和CoSQA数据集(python)）",
      "epoch": "待上传"
    },
    {
      "name": "text-to-code",
      "tags": {
        "data_type": ["code", "text"],
        "task_type": ["generation"],
        "domain": ["code_generation"],
        "input_type": "text",
        "output_type": "code"
      },
      "original_path": "Text-code/text-to-code",
      "description": "自然语言到代码的生成模型",
      "dataset": "文本描述-代码(c语言)对数据集",
      "epoch": "待上传"
    },
    {
      "name": "GraphMAE_QM9",
      "tags": {
        "data_type": ["graph"],
        "task_type": ["representation_learning", "autoencoder"],
        "domain": ["molecular_property"],
        "input_type": "molecular_graph",
        "output_type": "graph_embedding"
      },
      "original_path": "Graph/GraphMAE_QM9",
      "description": "在QM9数据集上训练的图掩码自编码器,通过对分子图中的原子的坐标以及类型进行预测实现自监督训练",
      "dataset": "分子属性预测数据集",
      "epoch": "待上传"
    },
    {
      "name": "AlexNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/AlexNet",
      "description": "2012年获得ImageNet冠军的经典模型，首次证明了深度学习在图像识别上的强大能力。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "DenseNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/DenseNet",
      "description": "每一层都直接与其他所有层相连，像搭积木一样层层堆叠，可以更好地学习图像特征。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "EfficientNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/EfficientNet",
      "description": "通过平衡网络的深度、宽度和图像分辨率，用更少的计算量达到更好的效果。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "GoogLeNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/GoogLeNet",
      "description": "谷歌开发的模型，像多个眼睛同时看图片的不同部分，既省资源又准确。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "LeNet5",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/LeNet5",
      "description": "深度学习领域的开山之作，虽然简单但奠定了现代CNN的基础架构。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "MobileNetv1",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "mobile_computing"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/MobileNetv1",
      "description": "专门为手机设计的轻量级模型，用特殊的卷积方式减少计算量。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "MobileNetv2",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "mobile_computing"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/MobileNetv2",
      "description": "MobileNet的升级版，增加了特征复用机制，性能更好。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "MobileNetv3",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "mobile_computing"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/MobileNetv3",
      "description": "结合自动搜索技术的新版本，自动找到最适合手机的网络结构。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "ResNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/ResNet",
      "description": "通过特殊的快捷连接解决深层网络训练难的问题，可以训练超级深的网络。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "SENet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/SENet",
      "description": "为网络添加了注意力机制，让模型能够关注图片中重要的部分。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "ShuffleNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "mobile_computing"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/ShuffleNet",
      "description": "通过巧妙地打乱和分组计算，实现了手机上的高效运行。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "ShuffleNetv2",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "mobile_computing"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/ShuffleNetv2",
      "description": "在原版基础上优化设计，速度更快，效果更好。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "SwinTransformer",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "transformer"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/SwinTransformer",
      "description": "把自然语言处理的先进技术用于图像，通过逐步关注图片不同区域来理解图像。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "VGG",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/VGG",
      "description": "用统一的小型卷积核堆叠成深层网络，结构简单但效果好。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "ViT",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision", "transformer"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/ViT",
      "description": "把图片切成小块后像读文章一样处理，是一种全新的图像处理方式。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    },
    {
      "name": "ZFNet",
      "tags": {
        "data_type": ["image"],
        "task_type": ["classification"],
        "domain": ["computer_vision"],
        "input_type": "image",
        "output_type": "class_label"
      },
      "original_path": "Image/ZFNet",
      "description": "通过可视化研究改进的AlexNet，帮助人们理解网络是如何看图片的。",
      "dataset": "CIFAR-10数据集",
      "epoch": "待补充"
    }
  ]
}