README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cats_vs_dogs
metrics:
  - accuracy
model-index:
  - name: vit-base-cats-vs-dogs
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cats_vs_dogs
          type: cats_vs_dogs
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9934510250569476

vit-base-cats-vs-dogs

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cats_vs_dogs dataset. It achieves the following results on the evaluation set:

• Loss: 0.0202
• Accuracy: 0.9935

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: 64
• eval_batch_size: 64
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.064	1.0	311	0.0483	0.9849
0.0622	2.0	622	0.0275	0.9903
0.0366	3.0	933	0.0262	0.9917
0.0294	4.0	1244	0.0219	0.9932
0.0161	5.0	1555	0.0202	0.9935

Framework versions

• Transformers 4.10.0.dev0
• Pytorch 1.9.0+cu102
• Datasets 1.11.1.dev0
• Tokenizers 0.10.3