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---
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
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