Instructions to use tanganke/convnext-base-224_eurosat_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_eurosat_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_eurosat_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_eurosat_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_eurosat_sgd_batch-size-64_lr-0.01_steps-4000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4157eba65bed65afec0e4cc5e543aac218846516a52ea7751ffb9aebfe461472
- Size of remote file:
- 350 MB
- SHA256:
- 073e730bb919bde5ee9783da4d3d4f158c73031284bc80e7f17307315fd88eb8
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