vit-base-patch16-224-finetuned-on-all-affectnet_short – AI Model by Mauregato | AlphaNeural AI

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Mauregato/
vit-base-patch16-224-finetuned-on-all-affectnet_short

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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vit-base-patch16-224-finetuned-on-all-affectnet_short

This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0520
Accuracy: 0.7259
Precision: 0.7293
Recall: 0.7259
F1: 0.7255

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: 2e-05
train_batch_size: 64
eval_batch_size: 16
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: 14
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.5399	1.0	91	1.4698	0.5097	0.5079	0.5097	0.4827
1.284	2.0	182	1.2026	0.6409	0.6514	0.6409	0.6226
1.2259	3.0	273	1.1367	0.6722	0.6749	0.6722	0.6694
1.1663	4.0	364	1.1086	0.6838	0.6903	0.6838	0.6814
1.1401	5.0	455	1.0782	0.7055	0.7070	0.7055	0.7042
1.1229	6.0	546	1.0734	0.7055	0.7093	0.7055	0.7036
1.0929	7.0	637	1.0674	0.7120	0.7147	0.7120	0.7099
1.0826	8.0	728	1.0601	0.7210	0.7226	0.7210	0.7191
1.0414	9.0	819	1.0558	0.7203	0.7211	0.7203	0.7196
1.0649	10.0	910	1.0499	0.7179	0.7194	0.7179	0.7175
1.0554	11.0	1001	1.0520	0.7259	0.7293	0.7259	0.7255
1.0496	12.0	1092	1.0466	0.7210	0.7212	0.7210	0.7204
1.064	13.0	1183	1.0502	0.7220	0.7235	0.7220	0.7211
1.0386	14.0	1274	1.0475	0.7206	0.7208	0.7206	0.7200

Framework versions

Transformers 4.29.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3