Image Classification
Transformers
TensorBoard
Safetensors
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
Instructions to use Aruno/gemini-beauty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aruno/gemini-beauty with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Aruno/gemini-beauty") 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("Aruno/gemini-beauty") model = AutoModelForImageClassification.from_pretrained("Aruno/gemini-beauty", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2906dd65c886c6cfd9453ed28019428e4154b5056bd03d7d409d42369dfed7af
- Size of remote file:
- 4.79 kB
- SHA256:
- a3ef97a2a4fc5a93e19a7cd53f60c4a3c6d85864c211e0c225157eb1a7461ff7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.