metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-cat-emotions
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: custom dataset
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6352941176470588

vit-base-cat-emotions

You can try out the model live here, and check out the GitHub repository for more details.

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

Loss: 1.0160
Accuracy: 0.6353

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3361	3.125	100	1.0125	0.6548
0.0723	6.25	200	0.9043	0.7381
0.0321	9.375	300	0.9268	0.7143

Framework versions

Transformers 4.44.1
Pytorch 2.2.2+cu118
Datasets 2.20.0
Tokenizers 0.19.1