Model save

README.md

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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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