End of training

README.md

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+---
+base_model: nateraw/vit-age-classifier
+tags:
+- generated_from_trainer
+datasets:
+- fair_face
+metrics:
+- accuracy
+model-index:
+- name: image_age_classification
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: fair_face
+      type: fair_face
+      config: '0.25'
+      split: train[:10000]
+      args: '0.25'
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5965
+---
+
+<!-- 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. -->
+
+# image_age_classification
+
+This model is a fine-tuned version of [nateraw/vit-age-classifier](https://huggingface.co/nateraw/vit-age-classifier) on the fair_face dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9479
+- Accuracy: 0.5965
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.0425        | 1.0   | 125  | 0.9358          | 0.6035   |
+| 0.8553        | 2.0   | 250  | 0.9411          | 0.5905   |
+| 0.8872        | 3.0   | 375  | 0.9626          | 0.6035   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

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