Image Classification
Transformers
Safetensors
vit
vision-transformer
flowers
Generated from Trainer
Eval Results (legacy)
Instructions to use lst0004/flower-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lst0004/flower-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lst0004/flower-vit") 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("lst0004/flower-vit") model = AutoModelForImageClassification.from_pretrained("lst0004/flower-vit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7820ac1e2e86f5d07ea328f474c0a8c1c67542e3c56d21fed6d23b38dee5c662
- Size of remote file:
- 5.2 kB
- SHA256:
- d8ea12d3eebe50ec740385df549f7c3a4a2f36d1cce2af390167dcd9ed45c8ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.