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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vit-base-patch16-224-in21k-finetuned-hongrui_mammogram_v_1
results: []
datasets:
- hongrui/mammogram_v_1
pipeline_tag: image-classification
---
<!-- 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-base-patch16-224-in21k-finetuned-hongrui_mammogram_v_1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7419
- Accuracy: 0.6991
- F1: 0.6767
- Precision: 0.6830
- Recall: 0.6991
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8576 | 1.0 | 171 | 0.8431 | 0.6678 | 0.6067 | 0.7751 | 0.6678 |
| 0.8297 | 2.0 | 342 | 0.7965 | 0.6791 | 0.6182 | 0.6758 | 0.6791 |
| 0.8303 | 3.0 | 513 | 0.7872 | 0.6842 | 0.6360 | 0.6704 | 0.6842 |
| 0.7814 | 4.0 | 684 | 0.7717 | 0.6843 | 0.6597 | 0.6601 | 0.6843 |
| 0.7768 | 5.0 | 855 | 0.7694 | 0.6906 | 0.6544 | 0.6775 | 0.6906 |
| 0.7415 | 6.0 | 1026 | 0.7572 | 0.6962 | 0.6718 | 0.6764 | 0.6962 |
| 0.7351 | 7.0 | 1197 | 0.7549 | 0.6922 | 0.6569 | 0.6648 | 0.6922 |
| 0.7197 | 8.0 | 1368 | 0.7479 | 0.6986 | 0.6855 | 0.6926 | 0.6986 |
| 0.7087 | 9.0 | 1539 | 0.7445 | 0.6979 | 0.6697 | 0.6792 | 0.6979 |
| 0.6977 | 10.0 | 1710 | 0.7419 | 0.6991 | 0.6767 | 0.6830 | 0.6991 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1 |