Instructions to use mikerol/beta14-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikerol/beta14-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mikerol/beta14-ViT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("mikerol/beta14-ViT") model = AutoModelForImageClassification.from_pretrained("mikerol/beta14-ViT") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: openai/clip-vit-base-patch32 | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: beta14-ViT | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # beta14-ViT | |
| This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4653 | |
| - Accuracy: 0.846 | |
| ## 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: 0.0001 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 64 | |
| - 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_steps: 100 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.7855 | 1.0 | 63 | 0.6587 | 0.795 | | |
| | 0.7609 | 2.0 | 126 | 0.7180 | 0.752 | | |
| | 0.4594 | 3.0 | 189 | 0.4653 | 0.846 | | |
| ### Framework versions | |
| - Transformers 4.57.3 | |
| - Pytorch 2.9.0+cpu | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 | |