ImageNet-1K图片分类任务:给定一张图片,要求模型从1K个候选类别中正确给出图片分类标签。
玩转OFA只需区区以下数行代码,就是如此轻松!如果你觉得还不够方便,请点击右上角Notebook
按钮,我们为你提供了配备好的环境(可选CPU/GPU),你只需要在notebook里输入提供的代码,就可以把OFA玩起来了!
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.outputs import OutputKeys
ofa_pipe = pipeline(Tasks.image_classification,model='damo/ofa_image-classification_imagenet_large_en')
result = ofa_pipe('http://xingchen-data.oss-cn-zhangjiakou.aliyuncs.com/maas/image-classification/image_classification.png')
print(result[OutputKeys.LABELS]) # 'forklift'
OFA(One-For-All)是通用多模态预训练模型,使用简单的序列到序列的学习框架统一模态(跨模态、视觉、语言等模态)和任务(如图片生成、视觉定位、图片描述、图片分类、文本生成等),详见我们发表于ICML 2022的论文:OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework,以及我们的官方Github仓库https://github.com/OFA-Sys/OFA。
Github  |  Paper   |  Blog
Model | Params-en | Params-zh | Backbone | Hidden size | Intermediate size | Num. of heads | Enc layers | Dec layers |
---|---|---|---|---|---|---|---|---|
OFATiny | 33M | - | ResNet50 | 256 | 1024 | 4 | 4 | 4 |
OFAMedium | 93M | - | ResNet101 | 512 | 2048 | 8 | 4 | 4 |
OFABase | 180M | 160M | ResNet101 | 768 | 3072 | 12 | 6 | 6 |
OFALarge | 470M | 440M | ResNet152 | 1024 | 4096 | 16 | 12 | 12 |
OFAHuge | 930M | - | ResNet152 | 1280 | 5120 | 16 | 24 | 12 |
OFA在ImageNet-1K上达到85.6的分类准确率,超出DINO、MoCo-v3等模型。分类效果展示如下:
本模型训练数据集是imagenet数据集。
finetune能力请参考OFA Tutorial 1.4节。
训练数据集自身有局限,有可能产生一些偏差,请用户自行评测后决定如何使用。
如果你觉得OFA好用,喜欢我们的工作,欢迎引用:
@article{wang2022ofa,
author = {Peng Wang and
An Yang and
Rui Men and
Junyang Lin and
Shuai Bai and
Zhikang Li and
Jianxin Ma and
Chang Zhou and
Jingren Zhou and
Hongxia Yang},
title = {OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence
Learning Framework},
journal = {CoRR},
volume = {abs/2202.03052},
year = {2022}
}