Instructions to use HalogenFlo/vit-emnist-byclass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HalogenFlo/vit-emnist-byclass with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HalogenFlo/vit-emnist-byclass") 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("HalogenFlo/vit-emnist-byclass") model = AutoModelForImageClassification.from_pretrained("HalogenFlo/vit-emnist-byclass", device_map="auto") - Notebooks
- Google Colab
- Kaggle
vit-emnist-byclass
This model was trained from scratch on an unknown 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: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Framework versions
- Transformers 4.57.2
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
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