Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
Chinese
English
vit
huggingpics
Eval Results (legacy)
Instructions to use Bazaar/cv_construction_vehicle_identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_construction_vehicle_identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_construction_vehicle_identification") 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_construction_vehicle_identification") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_construction_vehicle_identification") - Notebooks
- Google Colab
- Kaggle
cv_construction_vehicle_identification
使用HuggingPics微调生成
任务:工程车辆识别(目前能识别推土机、起重机、挖掘机、压路机、运输车、装载机)
使用方法:
from transformers import pipeline
classifier = pipeline('image-classification', model='Bazzar/cv_construction_vehicle_identification')
print(classifier('http://图片地址'))
Generated by HuggingPics
purpose:engineering vehicle identification(bulldozer、crane、excavator、road roller、transporters、wheel loaders)
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 示例图片
bulldozer
crane
excavator
road roller
transporters
wheel loaders
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Evaluation results
- Accuracyself-reported0.571





