metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - image-classification
  - pytorch
datasets:
  - cats_vs_dogs
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-cats-vs-dogs
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: cats_vs_dogs
          type: cats_vs_dogs
          args: default
        metrics:
          - type: accuracy
            value: 0.9934510250569476
            name: Accuracy

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 4.8.1
Pytorch 1.9.0+cu102
Datasets 1.11.1.dev0
Tokenizers 0.10.3