metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: visual_emotion_classification_vit_base_finetunned
    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: 0.51875

visual_emotion_classification_vit_base_finetunned

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: 1.2429
Accuracy: 0.5188

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.026	1.25	100	2.0071	0.275
1.8882	2.5	200	1.8921	0.3625
1.7186	3.75	300	1.7326	0.4188
1.5892	5.0	400	1.6242	0.475
1.4942	6.25	500	1.5443	0.5125
1.3825	7.5	600	1.4763	0.5062
1.3084	8.75	700	1.4554	0.4938
1.2388	10.0	800	1.4057	0.525
1.1519	11.25	900	1.3756	0.4938
1.1054	12.5	1000	1.3604	0.4875
1.0605	13.75	1100	1.3597	0.4938
1.016	15.0	1200	1.3370	0.4938
0.9601	16.25	1300	1.2981	0.4938
0.8445	17.5	1400	1.2420	0.5563
0.8514	18.75	1500	1.2485	0.5625
0.7899	20.0	1600	1.2861	0.4875
0.7459	21.25	1700	1.2860	0.4875
0.6917	22.5	1800	1.2335	0.5813
0.6864	23.75	1900	1.2726	0.5437
0.6414	25.0	2000	1.2215	0.5375
0.5583	26.25	2100	1.2756	0.5312
0.597	27.5	2200	1.2314	0.5375
0.5654	28.75	2300	1.3791	0.5125
0.5798	30.0	2400	1.1890	0.5687
0.5247	31.25	2500	1.2440	0.5687
0.5099	32.5	2600	1.2787	0.5625
0.496	33.75	2700	1.2628	0.55
0.479	35.0	2800	1.3420	0.4875
0.4685	36.25	2900	1.2817	0.5563
0.4375	37.5	3000	1.3122	0.525
0.4314	38.75	3100	1.1791	0.5563
0.4174	40.0	3200	1.2322	0.55
0.4019	41.25	3300	1.3871	0.5125
0.3738	42.5	3400	1.2854	0.5312
0.3938	43.75	3500	1.3057	0.5375
0.369	45.0	3600	1.2792	0.5437
0.3768	46.25	3700	1.2761	0.5625
0.3202	47.5	3800	1.2704	0.5375
0.3859	48.75	3900	1.2746	0.5312
0.3689	50.0	4000	1.3306	0.5563

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2