| --- |
| 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 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.39.0.dev0 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
