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beta15-ViT
mikerol/beta15-ViT
beta15-ViT is a image classification model from mikerol. Use it when you need a label for an image. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- clip
- Access
- Public
- Created
- Jan 7, 2026
- Updated
- Jan 7, 2026
- SHA
- c4ae09f4
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
beta15-ViT
This model is a fine-tuned version of openai/clip-vit-base-patch32 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3544
- Accuracy: 0.884
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: 5e-05
- 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 |
|---|---|---|---|---|
| 0.5833 | 1.0 | 63 | 0.7836 | 0.754 |
| 0.5564 | 2.0 | 126 | 0.5178 | 0.83 |
| 0.3288 | 3.0 | 189 | 0.3544 | 0.884 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Image Classification
- Library
- transformers
- Type
- clip
- Created
- Jan 7, 2026
- Updated
- Jan 7, 2026