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
File size: 556 Bytes
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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 |