metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: google-vit-base-patch16-224-cartoon-face-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: 0.9004629629629629
          - name: Precision
            type: precision
            value: 0.9066341895316832
          - name: Recall
            type: recall
            value: 0.9004629629629629
          - name: F1
            type: f1
            value: 0.8984296743444529

google-vit-base-patch16-224-cartoon-face-recognition

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3707
Accuracy: 0.9005
Precision: 0.9066
Recall: 0.9005
F1: 0.8984

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: 0.00012
train_batch_size: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	0.89	6	0.5459	0.8611	0.8683	0.8611	0.8577
0.0812	1.89	12	0.4703	0.8796	0.8833	0.8796	0.8764
0.0812	2.89	18	0.4430	0.8935	0.8969	0.8935	0.8906
0.0307	3.89	24	0.4045	0.8819	0.8849	0.8819	0.8767
0.0091	4.89	30	0.3672	0.9005	0.9025	0.9005	0.8980
0.0091	5.89	36	0.3841	0.9028	0.9125	0.9028	0.9011
0.0043	6.89	42	0.3926	0.9005	0.9073	0.9005	0.8972
0.0043	7.89	48	0.3786	0.8958	0.9005	0.8958	0.8931
0.0031	8.89	54	0.3791	0.9028	0.9091	0.9028	0.9007
0.002	9.89	60	0.3677	0.9028	0.9106	0.9028	0.9001
0.002	10.89	66	0.3740	0.9028	0.9099	0.9028	0.9007
0.0027	11.89	72	0.3869	0.8981	0.9043	0.8981	0.8956
0.0027	12.89	78	0.3801	0.8981	0.9021	0.8981	0.8954
0.004	13.89	84	0.3674	0.9051	0.9113	0.9051	0.9028
0.0024	14.89	90	0.3620	0.9051	0.9096	0.9051	0.9027
0.0024	15.89	96	0.3670	0.9028	0.9089	0.9028	0.9006
0.0021	16.89	102	0.3827	0.9005	0.9065	0.9005	0.8980
0.0021	17.89	108	0.3748	0.8981	0.9049	0.8981	0.8958
0.0022	18.89	114	0.3825	0.9028	0.9101	0.9028	0.9006
0.0019	19.89	120	0.3707	0.9005	0.9066	0.9005	0.8984

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.6.1
Tokenizers 0.13.1