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README.md
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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: food101_vit_model
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/26tpizu2)
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# food101_vit_model
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7311
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- Accuracy: 0.8536
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- Precision: 0.8531
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- Recall: 0.8536
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- F1: 0.8529
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 1.726 | 0.9994 | 1183 | 1.4984 | 0.7974 | 0.8021 | 0.7974 | 0.7906 |
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| 0.9996 | 1.9996 | 2367 | 0.8596 | 0.8417 | 0.8430 | 0.8417 | 0.8413 |
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| 0.8383 | 2.9981 | 3549 | 0.7311 | 0.8536 | 0.8531 | 0.8536 | 0.8529 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.1.2
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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