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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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- preprocessed1024_config |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: vit-mlo-512-breat_composition |
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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: preprocessed1024_config |
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type: preprocessed1024_config |
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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: |
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accuracy: 0.5791457286432161 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.5749067914290308 |
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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-mlo-512-breat_composition |
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This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3123 |
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- Accuracy: {'accuracy': 0.5791457286432161} |
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- F1: {'f1': 0.5749067914290308} |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:| |
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| 1.2679 | 1.0 | 796 | 1.0281 | {'accuracy': 0.5062814070351759} | {'f1': 0.38950358034816535} | |
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| 0.9805 | 2.0 | 1592 | 0.9240 | {'accuracy': 0.5672110552763819} | {'f1': 0.5273112700912543} | |
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| 0.9167 | 3.0 | 2388 | 0.9608 | {'accuracy': 0.5477386934673367} | {'f1': 0.45736748568671376} | |
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| 0.8292 | 4.0 | 3184 | 0.8973 | {'accuracy': 0.5891959798994975} | {'f1': 0.5783349603036094} | |
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| 0.7695 | 5.0 | 3980 | 1.0477 | {'accuracy': 0.5571608040201005} | {'f1': 0.5379432393338944} | |
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| 0.6912 | 6.0 | 4776 | 0.9479 | {'accuracy': 0.585427135678392} | {'f1': 0.5766494177636581} | |
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| 0.61 | 7.0 | 5572 | 1.1280 | {'accuracy': 0.5703517587939698} | {'f1': 0.5560158679652624} | |
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| 0.5591 | 8.0 | 6368 | 1.1866 | {'accuracy': 0.5741206030150754} | {'f1': 0.5541999644498281} | |
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| 0.5021 | 9.0 | 7164 | 1.1537 | {'accuracy': 0.582286432160804} | {'f1': 0.566315815243799} | |
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| 0.4262 | 10.0 | 7960 | 1.3123 | {'accuracy': 0.5791457286432161} | {'f1': 0.5749067914290308} | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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