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Zero Shot Image Classification

任务描述

零样本图像分类：模型在基于图文对的预训练后，可以在给定任意图片与候选标签列表的情况下，完成对图像的分类，而无需任何微调。

相关论文 Alec Radford, Jong Wook Kim, et al., Learning Transferable Visual Models From Natural Language Supervision, 2021.

已支持数据集性能

model type datasets Top1-accuracy stage example
clip clip_vit_b_32
clip_vit_b_16
clip_vit_l_14
clip_vit_l_14@336		 Cifar100 57.24%
61.41%
69.67%
68.19%		 eval
predict		 link
link

Cifar100

  • 数据集大小：161M，共60000张图片，100个类别
    • 训练集：50000张图片
    • 测试集：10000张图片
  • 数据格式：二进制文件
数据集目录格式
└─cifar-100-python
   ├─meta
   ├─test  
   └─train

快速任务接口

  • Trainer接口开启评估/推理：
import mindspore; mindspore.set_context(mode=0, device_id=0)
from mindformers import MindFormerBook
from mindformers.trainer import Trainer
from mindformers.tools.image_tools import load_image

# 显示Trainer的模型支持列表
MindFormerBook.show_trainer_support_model_list("zero_shot_image_classification")
# INFO - Trainer support model list for zero_shot_image_classification task is:
# INFO -    ['clip_vit_b_32', 'clip_vit_b_16', 'clip_vit_l_14', 'clip_vit_l_14@336']
# INFO - -------------------------------------

# 初始化trainer
trainer = Trainer(task='zero_shot_image_classification',
    model='clip_vit_b_32',
    eval_dataset='cifar-100-python'
)
img = load_image("https://ascend-repo-modelzoo.obs.cn-east-2."
          "myhuaweicloud.com/XFormer_for_mindspore/clip/sunflower.png")
trainer.evaluate()  #下载权重进行评估
# INFO - Top1 Accuracy=57.24%
trainer.predict(input_data=img)  #下载权重进行推理
# INFO - output result is saved at ./results.txt
  • pipeline接口开启快速推理
import mindspore; mindspore.set_context(mode=0, device_id=0)
from mindformers import pipeline, MindFormerBook
from mindformers.tools.image_tools import load_image

# 显示pipeline支持的模型列表
MindFormerBook.show_pipeline_support_model_list("zero_shot_image_classification")
# INFO - Pipeline support model list for zero_shot_image_classification task is:
# INFO -    ['clip_vit_b_32', 'clip_vit_b_16', 'clip_vit_l_14', 'clip_vit_l_14@336']
# INFO - -------------------------------------

# pipeline初始化
classifier = pipeline("zero_shot_image_classification",
                      model="clip_vit_b_32"
                      candidate_labels=["sunflower", "tree", "dog", "cat", "toy"])
img = load_image("https://ascend-repo-modelzoo.obs.cn-east-2."
          "myhuaweicloud.com/XFormer_for_mindspore/clip/sunflower.png")
classifier(img)
# 输出
# [[{'score': 0.99995565, 'label': 'sunflower'}, {'score': 2.5318595e-05, 'label': 'toy'},
# {'score': 9.903885e-06, 'label': 'dog'}, {'score': 6.75336e-06, 'label': 'tree'},
# {'score': 2.396818e-06, 'label': 'cat'}]]