Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/vit-base-beans") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nateraw/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download emissions.csv from nateraw/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
-
https://huggingface.co/nateraw/vit-base-beans/resolve/main/emissions.csv
- Command line
-
hf download hf://nateraw/vit-base-beans/emissions.csv
-
curl -L -o emissions.csv https://huggingface.co/nateraw/vit-base-beans/resolve/main/emissions.csv
308 Bytes
| timestamp,experiment_id,project_name,duration,emissions,energy_consumed,country_name,country_iso_code,region,on_cloud,cloud_provider,cloud_region | |
| 2021-08-31T12:54:49,a4449492-67ee-4738-a85d-5ac4861bcb29,codecarbon,124.76043105125427,0.005462426324374648,0.009645817277723201,USA,USA,Iowa,Y,gcp,us-central1 | |