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

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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vit-base-patch16-224-best-finetuned-on-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: 0.9712
—Accuracy: 0.6718
—Precision: 0.6698
—Recall: 0.6718
—F1: 0.6703

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
—trainbatchsize: 32
—evalbatchsize: 16
—seed: 42
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 128
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_ratio: 0.1
—num_epochs: 22

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.9968	1.0	32	1.9113	0.2754	0.2518	0.2754	0.2280
1.4178	2.0	64	1.2704	0.5049	0.5149	0.5049	0.4900
1.1751	3.0	96	1.1116	0.5841	0.5891	0.5841	0.5787
1.0127	4.0	128	1.0237	0.6162	0.6335	0.6162	0.6141
0.9969	5.0	160	0.9890	0.6259	0.6294	0.6259	0.6150
0.9376	6.0	192	0.9768	0.6190	0.6335	0.6190	0.6183
0.8299	7.0	224	0.9579	0.6357	0.6339	0.6357	0.6282
0.7645	8.0	256	0.9366	0.6489	0.6559	0.6489	0.6474
0.7944	9.0	288	0.9303	0.6443	0.6494	0.6443	0.6447
0.7334	10.0	320	0.9510	0.6546	0.6634	0.6546	0.6523
0.6596	11.0	352	0.9369	0.6449	0.6528	0.6449	0.6428
0.6781	12.0	384	0.9717	0.6368	0.6513	0.6368	0.6360
0.5688	13.0	416	0.9509	0.6540	0.6531	0.6540	0.6495
0.5766	14.0	448	0.9485	0.6615	0.6655	0.6615	0.6601
0.5529	15.0	480	0.9590	0.6569	0.6561	0.6569	0.6538
0.4998	16.0	512	0.9677	0.6512	0.6514	0.6512	0.6488
0.4908	17.0	544	0.9670	0.6638	0.6645	0.6638	0.6616
0.4682	18.0	576	0.9635	0.6678	0.6707	0.6678	0.6684
0.4761	19.0	608	0.9680	0.6667	0.6674	0.6667	0.6658
0.4161	20.0	640	0.9701	0.6713	0.6719	0.6713	0.6701
0.4295	21.0	672	0.9712	0.6718	0.6698	0.6718	0.6703
0.434	22.0	704	0.9755	0.6707	0.6705	0.6707	0.6690

Framework versions

—Transformers 4.29.0
—Pytorch 2.0.0+cu118
—Datasets 2.12.0
—Tokenizers 0.13.3