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--- |
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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: ditmodel |
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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: train |
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split: train |
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args: train |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9512195121951219 |
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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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# ditmodel |
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This model was trained from scratch on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1182 |
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- Accuracy: 0.9512 |
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- Weighted f1: 0.9515 |
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- Micro f1: 0.9512 |
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- Macro f1: 0.9473 |
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- Weighted recall: 0.9512 |
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- Micro recall: 0.9512 |
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- Macro recall: 0.9498 |
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- Weighted precision: 0.9527 |
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- Micro precision: 0.9512 |
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- Macro precision: 0.9458 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.1916 | 0.98 | 38 | 0.1396 | 0.9461 | 0.9465 | 0.9461 | 0.9408 | 0.9461 | 0.9461 | 0.9427 | 0.9487 | 0.9461 | 0.9412 | |
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| 0.1597 | 1.99 | 77 | 0.1227 | 0.9520 | 0.9523 | 0.9520 | 0.9485 | 0.9520 | 0.9520 | 0.9515 | 0.9541 | 0.9520 | 0.9472 | |
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| 0.1722 | 2.94 | 114 | 0.1182 | 0.9512 | 0.9515 | 0.9512 | 0.9473 | 0.9512 | 0.9512 | 0.9498 | 0.9527 | 0.9512 | 0.9458 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.6.1 |
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- Tokenizers 0.15.1 |
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