metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: emotion_classification_v1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train[:5000]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.59375
          - name: Precision
            type: precision
            value: 0.6599395444120348
          - name: Recall
            type: recall
            value: 0.59375
          - name: F1
            type: f1
            value: 0.5919790409999833

emotion_classification_v1

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: 1.1926
Accuracy: 0.5938
Precision: 0.6599
Recall: 0.5938
F1: 0.5920

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: 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	80	1.6474	0.3375	0.3120	0.3375	0.2259
No log	2.0	160	1.4434	0.4625	0.5606	0.4625	0.4112
No log	3.0	240	1.3266	0.4875	0.5296	0.4875	0.4516
No log	4.0	320	1.2547	0.5375	0.5836	0.5375	0.5342
No log	5.0	400	1.2195	0.5875	0.6815	0.5875	0.5900
No log	6.0	480	1.1895	0.5563	0.5709	0.5563	0.5424
1.2914	7.0	560	1.1572	0.5437	0.5607	0.5437	0.5431
1.2914	8.0	640	1.1822	0.5563	0.5602	0.5563	0.5515
1.2914	9.0	720	1.2712	0.55	0.5695	0.55	0.5530
1.2914	10.0	800	1.2196	0.5625	0.5701	0.5625	0.5559
1.2914	11.0	880	1.2460	0.5312	0.5584	0.5312	0.5357
1.2914	12.0	960	1.2473	0.5563	0.5710	0.5563	0.5553
0.5247	13.0	1040	1.2438	0.575	0.5908	0.575	0.5761
0.5247	14.0	1120	1.3033	0.5312	0.5391	0.5312	0.5305
0.5247	15.0	1200	1.2928	0.5625	0.5861	0.5625	0.5673

Framework versions

Transformers 4.41.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1