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vet-sm
dmartincc/vet-sm
vet-sm is a image classification model from dmartincc. 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
- Jul 1, 2024
- Updated
- Mar 17, 2025
- SHA
- ea36d38e
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. -->
vet-sm
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.8296
- Accuracy: 0.7440
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: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3577 | 1.0 | 375 | 1.3559 | 0.5333 |
| 1.1359 | 2.0 | 750 | 1.0537 | 0.6386 |
| 0.727 | 3.0 | 1125 | 0.8715 | 0.7156 |
| 0.3493 | 4.0 | 1500 | 0.8288 | 0.7355 |
| 0.1978 | 5.0 | 1875 | 0.8296 | 0.7440 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.21.0
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Jul 1, 2024
- Updated
- Mar 17, 2025