metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-mobile-eye-tracking-dataset-v2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9230769230769231

vit-base-patch16-224-in21k-finetuned-mobile-eye-tracking-dataset-v2

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

Loss: 0.2615
Accuracy: 0.9231

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	0.6641	0.6154
No log	2.0	4	0.6343	0.6154
No log	3.0	6	0.5990	0.6154
No log	4.0	8	0.5438	0.8462
No log	5.0	10	0.5108	0.9231
No log	6.0	12	0.4413	0.8462
No log	7.0	14	0.3947	0.8462
No log	8.0	16	0.3568	0.9231
No log	9.0	18	0.3297	0.9231
0.4923	10.0	20	0.3110	0.9231
0.4923	11.0	22	0.2988	0.9231
0.4923	12.0	24	0.2836	0.9231
0.4923	13.0	26	0.2702	0.9231
0.4923	14.0	28	0.2636	0.9231
0.4923	15.0	30	0.2615	0.9231

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0