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--- |
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base_model: microsoft/dit-base-finetuned-rvlcdip |
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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: 0.7575757575757576 |
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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 [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3033 |
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- Accuracy: 0.7576 |
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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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| 2.3577 | 0.96 | 18 | 2.0863 | 0.5114 | |
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| 2.0601 | 1.97 | 37 | 1.8154 | 0.6477 | |
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| 1.8068 | 2.99 | 56 | 1.5881 | 0.6705 | |
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| 1.5953 | 4.0 | 75 | 1.4112 | 0.7159 | |
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| 1.4304 | 4.96 | 93 | 1.3033 | 0.7576 | |
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| 1.3458 | 5.97 | 112 | 1.2401 | 0.75 | |
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| 1.3523 | 6.72 | 126 | 1.2240 | 0.7576 | |
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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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