Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use Bazaar/cv_canal_pollution_level with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_canal_pollution_level with Transformers:
# 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") - Notebooks
- Google Colab
- Kaggle
# 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")Quick Links
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
light pollution
moderate pollution
heavy pollution
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Evaluation results
- Accuracyself-reported0.903




# 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")