Instructions to use bswift/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bswift/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bswift/test") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, VisionTransformerForImageClassification processor = AutoImageProcessor.from_pretrained("bswift/test") model = VisionTransformerForImageClassification.from_pretrained("bswift/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"model_name": "ch-observations-12-12-2023-200530", "model_type": "vit", "architecture": "vit_large_patch16", "description": "Fine-tuned vit_large_patch16 model for ch-observations-12-12-2023-200530", "use_cases": ["Use case 1", "Use case 2"], "limitations": ["Limitation 1", "Limitation 2"], "ethics": ["Ethics 1", "Ethics 2"], "training_data": "420 images from 9 classes", "training_procedure": "Fine-tuned for 1 epochs with batch size 10 and base learning rate 0.005", "intended_use": "Intended for use with ch-observations", "authors": ["Author 1", "Author 2"], "references": ["Reference 1", "Reference 2"]} |