Instructions to use thilinadj/image_classification_tdj_fashion-mnist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thilinadj/image_classification_tdj_fashion-mnist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="thilinadj/image_classification_tdj_fashion-mnist") 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("thilinadj/image_classification_tdj_fashion-mnist") model = AutoModelForImageClassification.from_pretrained("thilinadj/image_classification_tdj_fashion-mnist") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 983226f0f615d5b4df6c3f47f7b1028b1e33248d76a89d35c8e19b5db932f70e
- Size of remote file:
- 3.39 kB
- SHA256:
- 1f9ec55b4ed23bd26acc98910df1da6ae52b17a039f0174873a5eb72954eaf44
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