cv_vit-base_image-classification_Dailylife-labels_flowers

Stefanieliang/cv_vit-base_image-classification_Dailylife-labels_flowers

PublisherStefanieliang

Download access requirementsModelScope source

自建1300类常见物体标签体系，覆盖常见的日用品，动物，植物，家具，设备，食物等物体，标签从海量中文互联网社区语料进行提取，保留了出现频率较高的常见物体名称。模型结构采用最新的ViT-Base结构。创空间快速可视化展示: ViT图像分类-中文-日常物品 NextViT实时图像分类-中文-日常物品

Image Analysis

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Model details

模型介绍

中文整理

日常物体识别模型介绍

自建1300类常见物体标签体系，覆盖常见的日用品，动物，植物，家具，设备，食物等物体，标签从海量中文互联网社区语料进行提取，保留了出现频率较高的常见物体名称。模型结构采用最新的ViT-Base结构。

创空间快速可视化展示: ViT图像分类-中文-日常物品

本系列还有如下模型，欢迎试用:

ViT图像分类-通用

NextViT实时图像分类-中文-日常物品

ConvNeXt图像分类-中文-垃圾分类

BEiTv2图像分类-通用-base

BEiTv2图像分类-通用-large

模型描述

采用Transformer经典的ViT-Base结构, 并采用了DeiT的知识蒸馏方式进行训练。

期望模型使用方式以及适用范围

本模型适用范围较广，覆盖大部分日常生活常见的物品类目，包括日用品，动物，植物，家具，设备，食物等。也可作为下游任务的预训练backbone。

如何使用

在ModelScope框架上，提供输入图片，即可通过简单的Pipeline调用来使用。

代码范例

模型局限性以及可能的偏差

支持1300类常见物体识别

训练数据介绍

140万包含常见物体的图像集

模型训练流程

主要训练参数参考DeiT论文的设置，除了weight decay在复现时设置为0.1，模型训练未使用pretrained参数进行初始化。

预处理

测试时主要的预处理如下：

Resize：先将原始图片的短边缩放至256

Normalize：图像归一化，减均值除以标准差

CenterCrop：裁切为224x224

数据评估及结果

模型在自建测试集进行测试，结果如下:

Model top-1 acc top-5 acc #params Remark

:--------: :-------: :--------: :-------: --------------

ViT-base 74.3 95.3 86M modelscope

模型训练

使用托管在modelscope DatasetHub上的小型数据集mini imagenet100进行finetune训练的示例代码:

训练说明见示例代码中的注释，更详细的训练说明和用法见官方的训练文档。训练过程产生的log和模型权重文件保存在work dir工作目录中，并以前缀为'best '的文件保存了在验证集上最优精度的权重。evaluate()默认使用精度最高的模型权重进行评估。

模型评估

使用训练好的模型对需要评估的数据集进行精度评估示例代码如下:

评估过程默认使用模型中自带的预训练权重进行评估。

Clone with HTTP

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使用前须知

运行环境

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