wmc_v2_vit_base_wm811k_cls_contra_learning_0916

Niraya666/wmc_v2_vit_base_wm811k_cls_contra_learning_0916

wmc_v2_vit_base_wm811k_cls_contra_learning_0916 is a machine learning model from Niraya666. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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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At a glance

Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Sep 16, 2024
Updated: Sep 16, 2024
SHA: 121930ae

Base models

google/vit-base-patch16-224

Model card

From the Hugging Face model README

wmc_v2_vit_base_wm811k_cls_contra_learning_0916

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

Loss: 0.0661
Accuracy: 0.9768
Precision: 0.9627
Recall: 0.9551
F1: 0.9585

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: 32
eval_batch_size: 32
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
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.1711	0.1697	100	0.6405	0.7559	0.6494	0.5790	0.5526
0.7143	0.3394	200	0.3116	0.8971	0.8478	0.7631	0.7576
0.584	0.5091	300	0.2060	0.9489	0.9137	0.8836	0.8940
0.4654	0.6788	400	0.1431	0.9603	0.9190	0.9289	0.9230
0.4465	0.8485	500	0.1176	0.9679	0.9458	0.9295	0.9373
0.3368	1.0182	600	0.1395	0.9550	0.9338	0.9244	0.9248
0.3741	1.1880	700	0.1541	0.9528	0.9287	0.9328	0.9269
0.3191	1.3577	800	0.1039	0.9697	0.9510	0.9453	0.9470
0.3354	1.5274	900	0.0952	0.9709	0.9530	0.9539	0.9529
0.3122	1.6971	1000	0.0799	0.9761	0.9456	0.9665	0.9556
0.295	1.8668	1100	0.0770	0.9758	0.9615	0.9534	0.9567
0.2993	2.0365	1200	0.0650	0.9794	0.9655	0.9597	0.9624
0.227	2.2062	1300	0.0717	0.9763	0.9598	0.9573	0.9584
0.2508	2.3759	1400	0.0653	0.9785	0.9605	0.9621	0.9613
0.3053	2.5456	1500	0.0629	0.9797	0.9623	0.9617	0.9620
0.2183	2.7153	1600	0.0676	0.9767	0.9597	0.9553	0.9572
0.219	2.8850	1700	0.0661	0.9768	0.9627	0.9551	0.9585

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 3.0.0
Tokenizers 0.19.1

Library: transformers
Type: vit
License: apache-2.0
Created: Sep 16, 2024
Updated: Sep 16, 2024