Instructions to use glopez/cifar-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use glopez/cifar-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="glopez/cifar-10") 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("glopez/cifar-10") model = AutoModelForImageClassification.from_pretrained("glopez/cifar-10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from glopez/cifar-10: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
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https://huggingface.co/glopez/cifar-10/resolve/main/README.md
- Command line
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hf download hf://glopez/cifar-10/README.md
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curl -L -o README.md https://huggingface.co/glopez/cifar-10/resolve/main/README.md
1.05 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
model-index:
- name: cifar-10
results: []
cifar-10
This model is a fine-tuned version of google/vit-base-patch16-224 on the cifar10 dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1