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vit-food101-finetuned
Rish74/vit-food101-finetuned
vit-food101-finetuned is a machine learning model from Rish74. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. 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
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- May 27, 2026
- Updated
- May 28, 2026
- SHA
- 001f5357
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. -->
vit-food101-finetuned
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: 0.9879
- Accuracy: 0.8124
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: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.6891 | 1.0 | 1184 | 2.5380 | 0.6677 |
| 1.5621 | 2.0 | 2368 | 1.4853 | 0.7644 |
| 1.1777 | 3.0 | 3552 | 1.1228 | 0.8017 |
| 1.1765 | 4.0 | 4736 | 1.0014 | 0.8142 |
| 1.1515 | 5.0 | 5920 | 0.9879 | 0.8124 |
Framework versions
- PEFT 0.19.1
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- peft
- License
- apache-2.0
- Created
- May 27, 2026
- Updated
- May 28, 2026