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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: microsoft/beit-base-patch16-224-pt22k-ft22k |
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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: drive_scene_time_day |
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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: 0.9890909090909091 |
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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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# drive_scene_time_day |
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This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0484 |
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- Accuracy: 0.9891 |
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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: 5 |
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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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| 0.0701 | 0.9855 | 34 | 0.0746 | 0.9818 | |
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| 0.0666 | 2.0 | 69 | 0.0632 | 0.9836 | |
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| 0.0565 | 2.9855 | 103 | 0.0556 | 0.9873 | |
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| 0.0412 | 4.0 | 138 | 0.0602 | 0.9864 | |
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| 0.0397 | 4.9275 | 170 | 0.0484 | 0.9891 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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