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my_awesome_food_model-full
cdb-0/my_awesome_food_model-full
my_awesome_food_model-full is a image classification model from cdb-0. 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.
<a href="https://huggingface.co/spaces/cdb-0/huggingface-static-de762a" target="blank"<img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio"…
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- vit
- Access
- Public
- Created
- May 17, 2026
- Updated
- May 17, 2026
- SHA
- 79c85e25
Base models
Model card
From the Hugging Face model README
<a href="https://huggingface.co/spaces/cdb-0/huggingface-static-de762a" target="_blank"><img src="https://raw.githubusercontent.com/gradio-app/trackio/refs/heads/main/trackio/assets/badge.png" alt="Visualize in Trackio" title="Visualize in Trackio" style="height: 40px;"/></a>
<!-- 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. -->my_awesome_food_model-full
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0707
- Accuracy: 0.7953
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: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9844 | 1.0 | 947 | 1.9589 | 0.7267 |
| 1.0701 | 2.0 | 1894 | 1.2317 | 0.7788 |
| 0.9445 | 3.0 | 2841 | 1.0707 | 0.7953 |
Framework versions
- Transformers 5.8.1
- Pytorch 2.12.0+cu132
- Datasets 3.6.0
- Tokenizers 0.22.2
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- License
- apache-2.0
- Created
- May 17, 2026
- Updated
- May 17, 2026