Instructions to use abletobetable/image_feature_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abletobetable/image_feature_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="abletobetable/image_feature_extractor") 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("abletobetable/image_feature_extractor") model = AutoModelForImageClassification.from_pretrained("abletobetable/image_feature_extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 141 Bytes
bea18c6 | 1 2 3 4 5 6 | ---
library_name: transformers
pipeline_tag: image-classification
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Finetuned Beit for product category classification based on it's image |