Model save

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit-base-patch16-224-ethos-25
+  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: 0.9170896785109983
+---
+
+<!-- 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-ethos-25
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2803
+- Accuracy: 0.9171
+
+## 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: 0.0002
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.606         | 0.99  | 43   | 1.3384          | 0.6387   |
+| 0.6334        | 1.99  | 86   | 0.5900          | 0.8519   |
+| 0.3928        | 2.98  | 129  | 0.4637          | 0.8739   |
+| 0.2361        | 4.0   | 173  | 0.3965          | 0.8909   |
+| 0.1816        | 4.99  | 216  | 0.4107          | 0.8782   |
+| 0.1253        | 5.99  | 259  | 0.3433          | 0.8976   |
+| 0.1255        | 6.98  | 302  | 0.3334          | 0.9069   |
+| 0.1009        | 8.0   | 346  | 0.3042          | 0.9154   |
+| 0.0812        | 8.99  | 389  | 0.2809          | 0.9146   |
+| 0.0698        | 9.94  | 430  | 0.2803          | 0.9171   |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.15.2