378A1_results

wuru330/378A1_results

378A1_results is a image classification model from wuru330. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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Hugging Face

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Jul 24, 2023
Updated: Jul 24, 2023
SHA: c7a150ff

Model card

From the Hugging Face model README

378A1_results

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4492
Accuracy: 0.9014

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2401	1.0	37	1.0427	0.6582
0.669	2.0	74	0.5486	0.8418
0.4662	3.0	111	0.4012	0.8690
0.3211	4.0	148	0.5338	0.7942
0.2136	5.0	185	0.3189	0.8861
0.1626	6.0	222	0.4406	0.8435
0.1042	7.0	259	0.3812	0.8741
0.0688	8.0	296	0.3501	0.8946
0.0425	9.0	333	0.3845	0.8912
0.0586	10.0	370	0.3640	0.8980
0.0276	11.0	407	0.3708	0.9031
0.0342	12.0	444	0.3862	0.9082
0.0251	13.0	481	0.5206	0.8776
0.0209	14.0	518	0.4078	0.8929
0.0173	15.0	555	0.4168	0.8895
0.0159	16.0	592	0.4108	0.8997
0.0151	17.0	629	0.4176	0.9014
0.014	18.0	666	0.4228	0.9014
0.0131	19.0	703	0.4266	0.9014
0.0125	20.0	740	0.4301	0.9014
0.012	21.0	777	0.4339	0.9014
0.0115	22.0	814	0.4372	0.9014
0.0111	23.0	851	0.4401	0.9014
0.0107	24.0	888	0.4424	0.9014
0.0101	25.0	925	0.4444	0.9014
0.01	26.0	962	0.4461	0.9014
0.01	27.0	999	0.4475	0.9014
0.0099	28.0	1036	0.4485	0.9014
0.0097	29.0	1073	0.4490	0.9014
0.0097	30.0	1110	0.4492	0.9014

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Jul 24, 2023
Updated: Jul 24, 2023