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--- |
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base_model: Tidzo/dit-base-rvlcdip-finetuned-grp-actual |
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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: dit-base-rvlcdip-finetuned-grp-actual |
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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: train |
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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: 0.9015151515151515 |
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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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# dit-base-rvlcdip-finetuned-grp-actual |
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This model is a fine-tuned version of [Tidzo/dit-base-rvlcdip-finetuned-grp-actual](https://huggingface.co/Tidzo/dit-base-rvlcdip-finetuned-grp-actual) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4601 |
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- Accuracy: 0.9015 |
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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: 7 |
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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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| 0.8692 | 0.96 | 18 | 0.6972 | 0.8561 | |
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| 0.7348 | 1.97 | 37 | 0.6350 | 0.8598 | |
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| 0.6655 | 2.99 | 56 | 0.5339 | 0.8712 | |
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| 0.7167 | 4.0 | 75 | 0.5046 | 0.8902 | |
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| 0.694 | 4.96 | 93 | 0.5026 | 0.8864 | |
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| 0.6638 | 5.97 | 112 | 0.4601 | 0.9015 | |
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| 0.6618 | 6.72 | 126 | 0.4582 | 0.8977 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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