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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-birads |
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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: 0.4667085427135678 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.3786054240333243 |
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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-birads |
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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: 1.0864 |
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- Accuracy: {'accuracy': 0.4667085427135678} |
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- F1: {'f1': 0.3786054240333243} |
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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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| 1.103 | 1.0 | 796 | 1.0452 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} | |
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| 1.0596 | 2.0 | 1592 | 1.0433 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} | |
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| 1.0547 | 3.0 | 2388 | 1.0361 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} | |
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| 1.047 | 4.0 | 3184 | 1.0395 | {'accuracy': 0.46796482412060303} | {'f1': 0.25128840471066954} | |
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| 1.0524 | 5.0 | 3980 | 1.0331 | {'accuracy': 0.4648241206030151} | {'f1': 0.298317360340153} | |
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| 1.0268 | 6.0 | 4776 | 1.0224 | {'accuracy': 0.47675879396984927} | {'f1': 0.23426509831984135} | |
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| 1.0043 | 7.0 | 5572 | 1.0609 | {'accuracy': 0.417713567839196} | {'f1': 0.3663405670841817} | |
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| 0.982 | 8.0 | 6368 | 1.0521 | {'accuracy': 0.44221105527638194} | {'f1': 0.3650005046420297} | |
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| 0.9315 | 9.0 | 7164 | 1.0473 | {'accuracy': 0.47738693467336685} | {'f1': 0.3727220695970696} | |
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| 0.9319 | 10.0 | 7960 | 1.0864 | {'accuracy': 0.4667085427135678} | {'f1': 0.3786054240333243} | |
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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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