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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: convnext-mlo-512-breat_composition-ordinal |
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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: 0.12185929648241206 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.05431131019036954 |
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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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# convnext-mlo-512-breat_composition-ordinal |
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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: 0.0275 |
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- Accuracy: {'accuracy': 0.12185929648241206} |
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- F1: {'f1': 0.05431131019036954} |
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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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| 0.0233 | 1.0 | 796 | 0.0269 | {'accuracy': 0.042085427135678394} | {'f1': 0.02019288728149488} | |
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| 0.0202 | 2.0 | 1592 | 0.0250 | {'accuracy': 0.09610552763819095} | {'f1': 0.043839541547277934} | |
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| 0.0183 | 3.0 | 2388 | 0.0248 | {'accuracy': 0.07977386934673367} | {'f1': 0.036940081442699245} | |
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| 0.0163 | 4.0 | 3184 | 0.0259 | {'accuracy': 0.17022613065326633} | {'f1': 0.07273215244229736} | |
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| 0.0144 | 5.0 | 3980 | 0.0258 | {'accuracy': 0.146356783919598} | {'f1': 0.06383561643835617} | |
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| 0.0117 | 6.0 | 4776 | 0.0249 | {'accuracy': 0.0992462311557789} | {'f1': 0.045142857142857144} | |
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| 0.0105 | 7.0 | 5572 | 0.0256 | {'accuracy': 0.10238693467336683} | {'f1': 0.04643874643874644} | |
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| 0.0084 | 8.0 | 6368 | 0.0261 | {'accuracy': 0.12185929648241206} | {'f1': 0.05431131019036954} | |
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| 0.0071 | 9.0 | 7164 | 0.0270 | {'accuracy': 0.10238693467336683} | {'f1': 0.04643874643874644} | |
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| 0.0065 | 10.0 | 7960 | 0.0275 | {'accuracy': 0.12185929648241206} | {'f1': 0.05431131019036954} | |
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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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