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
train_batch_size: 32
eval_batch_size: 16
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: 0.1
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

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05

train_batch_size: 32

eval_batch_size: 16

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: 0.1

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

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

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions