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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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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: fish_classification |
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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: imagefolder |
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type: imagefolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9969230769230769 |
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- name: F1 |
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type: f1 |
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value: 0.9970182569296375 |
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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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# fish_classification |
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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 imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2213 |
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- Accuracy: 0.9969 |
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- F1: 0.9970 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 1.5189 | 1.0 | 71 | 1.0828 | 0.9969 | 0.9970 | |
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| 0.7083 | 2.0 | 142 | 0.5398 | 0.9954 | 0.9955 | |
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| 0.3727 | 3.0 | 213 | 0.3473 | 0.9954 | 0.9955 | |
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| 0.2624 | 4.0 | 284 | 0.2734 | 0.9985 | 0.9985 | |
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| 0.2184 | 5.0 | 355 | 0.2401 | 0.9985 | 0.9985 | |
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| 0.1972 | 6.0 | 426 | 0.2238 | 0.9985 | 0.9985 | |
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| 0.1879 | 7.0 | 497 | 0.2213 | 0.9969 | 0.9970 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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