---
license: apache-2.0
base_model: facebook/vit-mae-base
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-mae-base-effusion-classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8173673328738801
---

<!-- 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-mae-base-effusion-classifier

This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4179
- Accuracy: 0.8174

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6554        | 1.0   | 362  | 0.6692          | 0.6030   |
| 0.569         | 2.0   | 725  | 0.5891          | 0.7023   |
| 0.6098        | 3.0   | 1088 | 0.5421          | 0.7367   |
| 0.4984        | 4.0   | 1451 | 0.5668          | 0.7043   |
| 0.4884        | 5.0   | 1813 | 0.6061          | 0.6844   |
| 0.4351        | 6.0   | 2176 | 0.4481          | 0.8098   |
| 0.4794        | 7.0   | 2539 | 0.4384          | 0.8084   |
| 0.4636        | 8.0   | 2902 | 0.4343          | 0.8077   |
| 0.4816        | 9.0   | 3264 | 0.5363          | 0.7491   |
| 0.5016        | 10.0  | 3627 | 0.4993          | 0.7677   |
| 0.4826        | 11.0  | 3990 | 0.4483          | 0.8043   |
| 0.4707        | 12.0  | 4353 | 0.4249          | 0.8112   |
| 0.4483        | 13.0  | 4715 | 0.4193          | 0.8160   |
| 0.419         | 14.0  | 5078 | 0.4146          | 0.8215   |
| 0.5039        | 15.0  | 5441 | 0.4188          | 0.8181   |
| 0.4111        | 16.0  | 5804 | 0.4459          | 0.8112   |
| 0.3293        | 17.0  | 6166 | 0.4228          | 0.8181   |
| 0.4171        | 18.0  | 6529 | 0.4239          | 0.8215   |
| 0.3375        | 19.0  | 6892 | 0.4162          | 0.8215   |
| 0.32          | 19.96 | 7240 | 0.4179          | 0.8174   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2