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:
- 3a4bab69e1254c0c9f0f4df36ba5917314641d5137235768f2a62045a6f3213d
- Size of remote file:
- 347 MB
- SHA256:
- 218e419bfa2dc5461678a9acfd0b461406a9590ffd2fbcfcace90bfa0d1e27d6
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