README.md

metadata

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

image_age_classification

This model is a fine-tuned version of 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