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---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-mlo-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: 0.4667085427135678
- name: F1
type: f1
value:
f1: 0.3786054240333243
---
<!-- 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-mlo-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: 1.0864
- Accuracy: {'accuracy': 0.4667085427135678}
- F1: {'f1': 0.3786054240333243}
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
| 1.103 | 1.0 | 796 | 1.0452 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
| 1.0596 | 2.0 | 1592 | 1.0433 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
| 1.0547 | 3.0 | 2388 | 1.0361 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
| 1.047 | 4.0 | 3184 | 1.0395 | {'accuracy': 0.46796482412060303} | {'f1': 0.25128840471066954} |
| 1.0524 | 5.0 | 3980 | 1.0331 | {'accuracy': 0.4648241206030151} | {'f1': 0.298317360340153} |
| 1.0268 | 6.0 | 4776 | 1.0224 | {'accuracy': 0.47675879396984927} | {'f1': 0.23426509831984135} |
| 1.0043 | 7.0 | 5572 | 1.0609 | {'accuracy': 0.417713567839196} | {'f1': 0.3663405670841817} |
| 0.982 | 8.0 | 6368 | 1.0521 | {'accuracy': 0.44221105527638194} | {'f1': 0.3650005046420297} |
| 0.9315 | 9.0 | 7164 | 1.0473 | {'accuracy': 0.47738693467336685} | {'f1': 0.3727220695970696} |
| 0.9319 | 10.0 | 7960 | 1.0864 | {'accuracy': 0.4667085427135678} | {'f1': 0.3786054240333243} |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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