metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hq_fer2013notestaugM
    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: 0.6998319625011055
          - name: Precision
            type: precision
            value: 0.7022095243425648
          - name: Recall
            type: recall
            value: 0.6998319625011055
          - name: F1
            type: f1
            value: 0.6999146124635052

hq_fer2013notestaugM

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: 0.8297
Accuracy: 0.6998
Precision: 0.7022
Recall: 0.6998
F1: 0.6999

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: 32
eval_batch_size: 32
seed: 17
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.2858	1.0	353	1.2814	0.5545	0.5432	0.5545	0.5122
1.0247	2.0	706	1.0343	0.6288	0.6235	0.6288	0.6136
0.9403	3.0	1059	0.9500	0.6607	0.6592	0.6607	0.6522
0.8501	4.0	1412	0.8971	0.6803	0.6761	0.6803	0.6760
0.8148	5.0	1765	0.8733	0.6857	0.6881	0.6857	0.6854
0.7898	6.0	2118	0.8526	0.6913	0.6911	0.6913	0.6888
0.7074	7.0	2471	0.8408	0.6959	0.6971	0.6959	0.6953
0.7273	8.0	2824	0.8361	0.6980	0.6971	0.6980	0.6949
0.6982	9.0	3177	0.8297	0.6998	0.7022	0.6998	0.6999
0.6994	10.0	3530	0.8287	0.6998	0.7002	0.6998	0.6991

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2