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vit-base-food-items-v1
heisenberg3376/vit-base-food-items-v1
vit-base-food-items-v1 is a image classification model from heisenberg3376. 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 17, 2024
- Updated
- Jul 17, 2024
- SHA
- 37b2dbda
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-base-food-items-v1
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset. It achieves the following results on the evaluation set:
- Loss: 0.4524
- Accuracy: 0.9091
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1773 | 0.6579 | 100 | 0.7280 | 0.8473 |
| 0.0589 | 1.3158 | 200 | 0.5529 | 0.8873 |
| 0.043 | 1.9737 | 300 | 0.4524 | 0.9091 |
| 0.0022 | 2.6316 | 400 | 0.5150 | 0.8909 |
| 0.0018 | 3.2895 | 500 | 0.4925 | 0.9018 |
| 0.0017 | 3.9474 | 600 | 0.4941 | 0.9018 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- Jul 17, 2024
- Updated
- Jul 17, 2024