Instructions to use Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/swinv2-base-patch4-window8-256-for-pre_evaluation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 29
Browse files- pytorch_model.bin +1 -1
pytorch_model.bin
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