Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Ferrysu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ferrysu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Ferrysu/results") 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("Ferrysu/results") model = AutoModelForImageClassification.from_pretrained("Ferrysu/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a53d62a0e22258f4b4a2c61661878b7a1af9ece9d8f3d603b9895316b0a3a0ed
- Size of remote file:
- 4.66 kB
- SHA256:
- a91d5c271938e56cf6c03172d902f3bc0f821eb60afd021105e90e29e083bb69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.