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README.md
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---
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tasks:
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- image-classification
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model-type:
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- Transformer
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domain:
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- cv
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frameworks:
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- pytorch
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backbone:
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- ViT-Base
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metrics:
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- accuracy
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finetune-support: True
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customized-quickstart: True
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integrating: False
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license: Apache License 2.0
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tags:
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- Alibaba
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- Image classification
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- Transformer
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- Dailylife tags
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datasets:
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evaluation:
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- online dataset
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train:
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- tany0699/dailytags
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test:
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- tany0699/dailytags
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widgets:
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- task: image-classification
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inputs:
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- type: image
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examples:
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- name: 1
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inputs:
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- name: image
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data: git://resources/test.jpg
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---
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# 日常物体识别模型介绍
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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。
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创空间快速可视化展示: [ViT图像分类-中文-日常物品](https://modelscope.cn/studios/tany0699/cv_vit-base_image-classification_Dailylife-labels/summary)
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本系列还有如下模型，欢迎试用:
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- [ViT图像分类-通用](https://modelscope.cn/models/damo/cv_vit-base_image-classification_ImageNet-labels/summary)
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- [NextViT实时图像分类-中文-日常物品](https://modelscope.cn/models/damo/cv_nextvit-small_image-classification_Dailylife-labels/summary)
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- [ConvNeXt图像分类-中文-垃圾分类](https://modelscope.cn/models/damo/cv_convnext-base_image-classification_garbage/summary)
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- [BEiTv2图像分类-通用-base](https://modelscope.cn/models/damo/cv_beitv2-base_image-classification_patch16_224_pt1k_ft22k_in1k/summary)
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- [BEiTv2图像分类-通用-large](https://modelscope.cn/models/damo/cv_beitv2-large_image-classification_patch16_224_pt1k_ft22k_in1k/summary)
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## 模型描述
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采用Transformer经典的[ViT-Base](https://github.com/google-research/vision_transformer)结构, 并采用了DeiT的知识蒸馏方式进行训练。
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<img src="./resources/overview.jpg" alt="overview"/>
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## 期望模型使用方式以及适用范围
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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。
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### 如何使用
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在ModelScope框架上，提供输入图片，即可通过简单的Pipeline调用来使用。
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#### 代码范例
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```python
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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img_path = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/bird.JPEG'
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image_classification = pipeline(Tasks.image_classification,
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model='damo/cv_vit-base_image-classification_Dailylife-labels')
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result = image_classification(img_path)
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print(result)
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```
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### 模型局限性以及可能的偏差
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- 支持1300类常见物体识别
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## 训练数据介绍
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- 140万包含常见物体的图像集
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## 模型训练流程
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- 主要训练参数参考[DeiT论文](https://arxiv.org/abs/2012.12877)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。
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### 预处理
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测试时主要的预处理如下：
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- Resize：先将原始图片的短边缩放至256
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- Normalize：图像归一化，减均值除以标准差
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- CenterCrop：裁切为224x224
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## 数据评估及结果
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模型在自建测试集进行测试，结果如下:
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| Model | top-1 acc | top-5 acc | #params | Remark |
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|:--------:|:-------:|:--------:|:-------:|--------------|
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| ViT-base | 74.3 | 95.3 | 86M | modelscope |
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## 模型训练
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使用托管在modelscope DatasetHub上的小型数据集[mini_imagenet100](https://modelscope.cn/datasets/tany0699/mini_imagenet100/summary)进行finetune训练的示例代码:
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```python
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from modelscope.msdatasets import MsDataset
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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import tempfile
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model_id = 'damo/cv_vit-base_image-classification_Dailylife-labels'
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# 加载数据
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ms_train_dataset = MsDataset.load(
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'mini_imagenet100', namespace='tany0699',
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subset_name='default', split='train') # 加载训练集
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ms_val_dataset = MsDataset.load(
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'mini_imagenet100', namespace='tany0699',
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subset_name='default', split='validation') # 加载验证集
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tmp_dir = tempfile.TemporaryDirectory().name # 使用临时目录作为工作目录
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# 修改配置文件
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def cfg_modify_fn(cfg):
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cfg.train.dataloader.batch_size_per_gpu = 32 # batch大小
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cfg.train.dataloader.workers_per_gpu = 2 # 每个gpu的worker数目
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cfg.train.max_epochs = 1 # 最大训练epoch数
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cfg.model.mm_model.head.num_classes = 100 # 分类数
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cfg.model.mm_model.train_cfg.augments[0].num_classes = 100 # 分类数
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cfg.model.mm_model.train_cfg.augments[1].num_classes = 100 # 分类数
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cfg.train.optimizer.lr = 1e-4 # 学习率
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cfg.train.lr_config.warmup_iters = 1 # 预热次数
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cfg.train.evaluation.metric_options = {'topk': (1, 5)} # 训练时的评估指标
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cfg.evaluation.metric_options = {'topk': (1, 5)} # 评估时的评估指标
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return cfg
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# 构建训练器
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kwargs = dict(
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model=model_id, # 模型id
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work_dir=tmp_dir, # 工作目录
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train_dataset=ms_train_dataset, # 训练集
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eval_dataset=ms_val_dataset, # 验证集
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cfg_modify_fn=cfg_modify_fn, # 用于修改训练配置文件的回调函数
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model_revision='v1.0.2'
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)
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trainer = build_trainer(name=Trainers.image_classification, default_args=kwargs)
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# 进行训练
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trainer.train()
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# 进行评估
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result = trainer.evaluate()
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print('result:', result)
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```
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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](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AE%AD%E7%BB%83Train)。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。
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## 模型评估
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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:
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```python
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from modelscope.msdatasets import MsDataset
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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import tempfile
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model_id = 'damo/cv_vit-base_image-classification_Dailylife-labels'
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# 加载用于评估的数据集
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ms_val_dataset = MsDataset.load(
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'dailytags', namespace='tany0699',
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subset_name='default', split='validation')
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tmp_dir = tempfile.TemporaryDirectory().name # 使用临时目录作为工作目录
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# 构建训练器
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kwargs = dict(
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model=model_id, # 模型id
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work_dir=tmp_dir, # 工作目录
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train_dataset=None,
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eval_dataset=ms_val_dataset, # 评估的数据集
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model_revision='v1.0.2'
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)
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trainer = build_trainer(name=Trainers.image_classification, default_args=kwargs)
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# 开始评估
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result = trainer.evaluate()
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print('result:', result)
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```
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评估过程默认使用模型中自带的预训练权重进行评估。
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#### Clone with HTTP
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```bash
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git clone https://www.modelscope.cn/damo/cv_vit-base_image-classification_Dailylife-labels.git
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```
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