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vit-car-classification
pericmi1/vit-car-classification
vit-car-classification is a image classification model from pericmi1. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026
- SHA
- 54156ef2
Base models
Datasets
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. -->
vit-car-classification
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.3580
- Accuracy: 0.44
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: 8
- eval_batch_size: 8
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6988 | 1.0 | 19 | 1.5246 | 0.32 |
| 1.1305 | 2.0 | 38 | 1.4436 | 0.36 |
| 0.7678 | 3.0 | 57 | 1.4152 | 0.4 |
| 0.5965 | 4.0 | 76 | 1.3638 | 0.4 |
| 0.4967 | 5.0 | 95 | 1.3580 | 0.44 |
Framework versions
- Transformers 5.5.0
- Pytorch 2.8.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026