Instructions to use tanganke/convnext-base-224_gtsrb_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_gtsrb_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_gtsrb_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_gtsrb_sgd_batch-size-64_lr-0.01_steps-4000") model = AutoModelForImageClassification.from_pretrained("tanganke/convnext-base-224_gtsrb_sgd_batch-size-64_lr-0.01_steps-4000") - Notebooks
- Google Colab
- Kaggle
convnext-base-224_gtsrb_sgd_batch-size-64_lr-0.01_steps-4000 / events.out.tfevents.1767759872.pt-37e6e05bf63a49588b5dbb472d0ac23f-worker-0.67651.0
- Xet hash:
- e8c71da76e61c315080c5e8d7630d97d6ef32be5eee33f19b44d36523d804c61
- Size of remote file:
- 2.81 MB
- SHA256:
- 8d383ce50b19d51ddf6927bd64f27833c3b585d2284e607243985be000137166
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