How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="Bazaar/cv_canal_pollution_level")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification

processor = AutoImageProcessor.from_pretrained("Bazaar/cv_canal_pollution_level")
model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_canal_pollution_level")
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cv_canal_pollution_level

使用HuggingPics微调生成

任务:河道污染等级分类(无污染、轻度污染、中度污染、重度污染)

使用方法:

from transformers import pipeline

classifier = pipeline('image-classification', model='Bazzar/cv_canal_pollution_level')
print(classifier('http://图片地址'))

Autogenerated by HuggingPics🤗🖼️

Create your own image classifier for anything by running the demo on Google Colab.

Report any issues with the demo at the github repo.

Example Images

no pollution

no pollution

light pollution

light pollution

moderate pollution

moderate pollution

heavy pollution

heavy pollution

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Model size
85.8M params
Tensor type
F32
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Evaluation results