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
| pipeline_tag: image-classification | |
| # ch-observations-12-12-2023-200530 | |
| ## 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 |