Instructions to use eligapris/v-mdd-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eligapris/v-mdd-2000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="eligapris/v-mdd-2000") 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("eligapris/v-mdd-2000") model = AutoModelForImageClassification.from_pretrained("eligapris/v-mdd-2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
transformers
Browse files
README.md
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- accuracy
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- bertscore
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pipeline_tag: image-classification
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---
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# Model Trained Using AutoTrain
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- accuracy
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- bertscore
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pipeline_tag: image-classification
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library_name: transformers
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# Model Trained Using AutoTrain
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