vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred – AI Model by venetis | AlphaNeural AI

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venetis/
vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred

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: 0.4670
Accuracy: 0.8781
Precision: 0.8768
Recall: 0.8781
F1: 0.8758

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: 5e-05
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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
3.5529	1.0	839	3.3687	0.3096	0.2809	0.3096	0.2246
1.7855	2.0	1678	1.6042	0.6378	0.6187	0.6378	0.5996
1.1054	3.0	2517	1.0105	0.7556	0.7512	0.7556	0.7385
0.8179	4.0	3356	0.7794	0.8033	0.8020	0.8033	0.7934
0.6057	5.0	4195	0.6479	0.8294	0.8274	0.8294	0.8212
0.4709	6.0	5034	0.5817	0.8478	0.8477	0.8478	0.8428
0.3962	7.0	5873	0.5333	0.8571	0.8570	0.8571	0.8527
0.346	8.0	6712	0.5073	0.8638	0.8647	0.8638	0.8615
0.2772	9.0	7551	0.4881	0.8681	0.8679	0.8681	0.8656
0.2136	10.0	8390	0.4777	0.8719	0.8718	0.8719	0.8689
0.1937	11.0	9229	0.4737	0.8734	0.8731	0.8734	0.8703
0.1754	12.0	10068	0.4604	0.8758	0.8750	0.8758	0.8733
0.1111	13.0	10907	0.4561	0.8790	0.8782	0.8790	0.8768
0.1128	14.0	11746	0.4519	0.8808	0.8799	0.8808	0.8787
0.1018	15.0	12585	0.4497	0.8813	0.8805	0.8813	0.8794

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2