Zero Shot Image Classification

数据集目录格式
└─cifar-100-python
   ├─meta
   ├─test  
   └─train  

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

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'}]]