Model save

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: vit_epochs5_batch32_lr5e-05_size224_tiles1_seed1_classic_image_classification_local
+  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: 0.9949333333333333
+---
+
+<!-- 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_epochs5_batch32_lr5e-05_size224_tiles1_seed1_classic_image_classification_local
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0250
+- Accuracy: 0.9949
+
+## 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-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0735        | 1.0   | 469  | 0.0778          | 0.9763   |
+| 0.0205        | 2.0   | 938  | 0.0415          | 0.9896   |
+| 0.0011        | 3.0   | 1407 | 0.0229          | 0.9947   |
+| 0.0008        | 4.0   | 1876 | 0.0247          | 0.9949   |
+| 0.0007        | 5.0   | 2345 | 0.0250          | 0.9949   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.2.2
+- Datasets 2.18.0
+- Tokenizers 0.19.1

model.safetensors

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