Instructions to use tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000") 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("tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_mnist_sgd_batch-size-64_lr-0.01_steps-4000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 533b1fbf242f06fb5af7abe0bfbf73f18c3a7d1a91edad2e34c4d4d2c73a9870
- Size of remote file:
- 350 MB
- SHA256:
- 59acec4ad4fbd397161ef7e9e861ac2fa0326f415cc9020419dee5b4077cc7f8
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