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: 0.8294
Accuracy: 0.7052
Precision: 0.7048
Recall: 0.7052
F1: 0.7036

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
1.2982	1.0	353	1.2708	0.5635	0.5107	0.5635	0.5168
1.0218	2.0	706	1.0159	0.6411	0.6397	0.6411	0.6301
0.9437	3.0	1059	0.9452	0.6631	0.6698	0.6631	0.6556
0.8282	4.0	1412	0.8873	0.6829	0.6798	0.6829	0.6743
0.7717	5.0	1765	0.8612	0.6884	0.6888	0.6884	0.6835
0.7678	6.0	2118	0.8473	0.6985	0.6989	0.6985	0.6966
0.7096	7.0	2471	0.8363	0.7018	0.7001	0.7018	0.6989
0.6803	8.0	2824	0.8333	0.7036	0.7036	0.7036	0.7019
0.6521	9.0	3177	0.8309	0.7050	0.7039	0.7050	0.7028
0.6671	10.0	3530	0.8294	0.7052	0.7048	0.7052	0.7036

Framework versions

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

Piro17/hq_fer2013notest

hq_fer2013notest

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Piro17/
hq_fer2013notest