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---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-cc-512-birads
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: preprocessed1024_config
type: preprocessed1024_config
args: default
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.4943467336683417
- name: F1
type: f1
value:
f1: 0.3929699341372617
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-cc-512-birads
This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1133
- Accuracy: {'accuracy': 0.4943467336683417}
- F1: {'f1': 0.3929699341372617}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
| 1.1037 | 1.0 | 796 | 1.0357 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
| 1.0588 | 2.0 | 1592 | 1.0446 | {'accuracy': 0.4623115577889447} | {'f1': 0.33094476503399495} |
| 1.0486 | 3.0 | 2388 | 1.0408 | {'accuracy': 0.47361809045226133} | {'f1': 0.3313643442345453} |
| 1.0288 | 4.0 | 3184 | 1.0186 | {'accuracy': 0.5050251256281407} | {'f1': 0.3404676010455165} |
| 1.0284 | 5.0 | 3980 | 1.0288 | {'accuracy': 0.5037688442211056} | {'f1': 0.3406391773730375} |
| 0.997 | 6.0 | 4776 | 1.0183 | {'accuracy': 0.5087939698492462} | {'f1': 0.3539488153998284} |
| 0.9682 | 7.0 | 5572 | 1.0965 | {'accuracy': 0.4566582914572864} | {'f1': 0.3695106771946128} |
| 0.9313 | 8.0 | 6368 | 1.0554 | {'accuracy': 0.4962311557788945} | {'f1': 0.38158088397057704} |
| 0.8938 | 9.0 | 7164 | 1.0930 | {'accuracy': 0.4943467336683417} | {'f1': 0.38196414933207573} |
| 0.8697 | 10.0 | 7960 | 1.1133 | {'accuracy': 0.4943467336683417} | {'f1': 0.3929699341372617} |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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