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: 1.2755
Accuracy: 0.6062

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: 0.0005
train_batch_size: 32
eval_batch_size: 32
seed: 42
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: 0.3
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8344	1.0	5	1.1193	0.5813
0.7539	2.0	10	1.2210	0.5563
0.6334	3.0	15	1.2974	0.5188
0.6163	4.0	20	1.1309	0.6
0.4633	5.0	25	1.2804	0.5312
0.4066	6.0	30	1.1664	0.6
0.335	7.0	35	1.1741	0.6062
0.3484	8.0	40	1.1644	0.6125
0.3134	9.0	45	1.2799	0.55
0.2689	10.0	50	1.2276	0.6

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

Zekrom997/emotion_recognition_I

emotion_recognition_I

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Zekrom997/
emotion_recognition_I