metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: finetuned-affecthq
    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: 0.7179302910528207
          - name: Precision
            type: precision
            value: 0.7173911115103917
          - name: Recall
            type: recall
            value: 0.7179302910528207
          - name: F1
            type: f1
            value: 0.7166821507529032

finetuned-affecthq

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: 0.8116
Accuracy: 0.7179
Precision: 0.7174
Recall: 0.7179
F1: 0.7167

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
1.5413	1.0	174	1.4810	0.4898	0.4867	0.4898	0.4409
1.0367	2.0	348	1.0571	0.6155	0.6172	0.6155	0.6041
0.9534	3.0	522	0.9673	0.6475	0.6476	0.6475	0.6375
0.8532	4.0	696	0.9056	0.6748	0.6710	0.6748	0.6704
0.8211	5.0	870	0.8707	0.6903	0.6912	0.6903	0.6836
0.7797	6.0	1044	0.8472	0.7050	0.7050	0.7050	0.7019
0.7816	7.0	1218	0.8298	0.7111	0.7099	0.7111	0.7096
0.7135	8.0	1392	0.8186	0.7111	0.7116	0.7111	0.7105
0.6697	9.0	1566	0.8143	0.7140	0.7124	0.7140	0.7126
0.6765	10.0	1740	0.8116	0.7179	0.7174	0.7179	0.7167

Framework versions

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