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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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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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model-index: |
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- name: vit-base-patch16-224-finetuned-hateful-meme-restructured |
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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: 0.552 |
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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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# vit-base-patch16-224-finetuned-hateful-meme-restructured |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7152 |
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- Accuracy: 0.552 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.6546 | 0.99 | 66 | 0.7185 | 0.52 | |
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| 0.6222 | 2.0 | 133 | 0.7152 | 0.552 | |
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| 0.5986 | 2.99 | 199 | 0.7344 | 0.542 | |
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| 0.5535 | 4.0 | 266 | 0.7782 | 0.514 | |
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| 0.5377 | 4.99 | 332 | 0.8329 | 0.514 | |
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| 0.5115 | 6.0 | 399 | 0.7596 | 0.528 | |
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| 0.5133 | 6.99 | 465 | 0.8151 | 0.512 | |
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| 0.511 | 8.0 | 532 | 0.7897 | 0.538 | |
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| 0.4712 | 8.99 | 598 | 0.8539 | 0.514 | |
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| 0.4626 | 9.92 | 660 | 0.8449 | 0.522 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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