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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- food101 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: my_awesome_food_model |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: food101 |
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type: food101 |
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config: default |
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split: train[:5000] |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8855567868882221 |
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- name: Recall |
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type: recall |
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value: 0.887 |
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- name: F1 |
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type: f1 |
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value: 0.8818977914615195 |
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- name: Accuracy |
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type: accuracy |
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value: 0.887 |
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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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# my_awesome_food_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 the food101 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6405 |
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- Precision: 0.8856 |
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- Recall: 0.887 |
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- F1: 0.8819 |
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- Accuracy: 0.887 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 2.7494 | 0.99 | 62 | 2.5554 | 0.7488 | 0.829 | 0.7859 | 0.829 | |
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| 1.9011 | 2.0 | 125 | 1.8058 | 0.8825 | 0.878 | 0.8645 | 0.878 | |
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| 1.6532 | 2.98 | 186 | 1.6405 | 0.8856 | 0.887 | 0.8819 | 0.887 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.13.3 |
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