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_recognition
    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: 0.6125

emotion_recognition

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: 1.2014
Accuracy: 0.6125

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: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0842	1.0	10	2.0668	0.175
2.039	2.0	20	2.0070	0.2875
1.9285	3.0	30	1.8789	0.4062
1.7699	4.0	40	1.6942	0.425
1.6135	5.0	50	1.5758	0.4313
1.5056	6.0	60	1.4884	0.55
1.3896	7.0	70	1.3999	0.5437
1.2804	8.0	80	1.3563	0.5437
1.2043	9.0	90	1.3244	0.55
1.1231	10.0	100	1.2775	0.6062
1.0652	11.0	110	1.2567	0.575
1.0005	12.0	120	1.2833	0.5563
0.9878	13.0	130	1.2277	0.5687
0.9714	14.0	140	1.2557	0.5563
0.9057	15.0	150	1.2187	0.6125
0.8854	16.0	160	1.2612	0.5437
0.8478	17.0	170	1.2450	0.5437
0.8601	18.0	180	1.2456	0.5375
0.8498	19.0	190	1.2413	0.5875
0.8775	20.0	200	1.1928	0.6

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1