Instructions to use yusx-swapp/ofm-vit-base-patch16-224-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yusx-swapp/ofm-vit-base-patch16-224-cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yusx-swapp/ofm-vit-base-patch16-224-cifar100") 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("yusx-swapp/ofm-vit-base-patch16-224-cifar100") model = AutoModelForImageClassification.from_pretrained("yusx-swapp/ofm-vit-base-patch16-224-cifar100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ofm-vit-base-patch16-224-cifar100
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: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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