metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: emotion_classification
    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: 0.6

emotion_classification

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: 1.2383
Accuracy: 0.6

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0769	1.0	10	2.0617	0.1812
2.0383	2.0	20	2.0104	0.3
1.9423	3.0	30	1.8932	0.425
1.7923	4.0	40	1.7442	0.475
1.6547	5.0	50	1.6047	0.4875
1.5297	6.0	60	1.5184	0.5437
1.4345	7.0	70	1.4392	0.5625
1.337	8.0	80	1.3847	0.5875
1.2722	9.0	90	1.3442	0.55
1.217	10.0	100	1.3058	0.5625
1.1497	11.0	110	1.2914	0.55
1.0977	12.0	120	1.2377	0.6125
1.0507	13.0	130	1.2253	0.5687
1.0268	14.0	140	1.2269	0.5938
0.967	15.0	150	1.2260	0.5938
0.9269	16.0	160	1.2421	0.5687
0.9102	17.0	170	1.2218	0.5687
0.8883	18.0	180	1.2207	0.5687
0.8633	19.0	190	1.1933	0.6062
0.8557	20.0	200	1.1830	0.575

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3