wmc_v2_vit_base_wm811k_cls_contra_learning_0916_9cls

Niraya666/wmc_v2_vit_base_wm811k_cls_contra_learning_0916_9cls

wmc_v2_vit_base_wm811k_cls_contra_learning_0916_9cls 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: ecc9a655

Base models

google/vit-base-patch16-224

Model card

From the Hugging Face model README

wmc_v2_vit_base_wm811k_cls_contra_learning_0916_9cls

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: 0.1013
Accuracy: 0.9670
Precision: 0.9209
Recall: 0.8649
F1: 0.8808

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
0.3763	0.1079	100	0.9646	0.6825	0.1404	0.1291	0.1179
0.2651	0.2158	200	0.6134	0.7668	0.3945	0.2648	0.2505
0.1556	0.3237	300	0.2849	0.9183	0.6474	0.5500	0.5700
0.1999	0.4316	400	0.2655	0.9021	0.7646	0.5318	0.5426
0.1746	0.5395	500	0.2362	0.9086	0.7687	0.6036	0.6230
0.1733	0.6474	600	0.2026	0.9509	0.7935	0.7895	0.7860
0.1048	0.7553	700	0.1498	0.9563	0.8978	0.7432	0.7662
0.1751	0.8632	800	0.1688	0.9495	0.8475	0.7802	0.7727
0.1087	0.9711	900	0.1966	0.9220	0.8840	0.6922	0.6952
0.1367	1.0790	1000	0.1605	0.9423	0.8138	0.8021	0.7573
0.1251	1.1869	1100	0.1698	0.9313	0.7926	0.8010	0.7637
0.1383	1.2948	1200	0.1252	0.9625	0.8940	0.8389	0.8525
0.1173	1.4028	1300	0.1372	0.9476	0.8857	0.7698	0.7774
0.1014	1.5107	1400	0.1104	0.9655	0.9173	0.8072	0.8257
0.1073	1.6186	1500	0.1222	0.9651	0.8932	0.8670	0.8792
0.1093	1.7265	1600	0.1270	0.9517	0.8591	0.8431	0.8316
0.0832	1.8344	1700	0.1128	0.9645	0.9080	0.8533	0.8707
0.0972	1.9423	1800	0.1040	0.9704	0.9309	0.8473	0.8744
0.0771	2.0502	1900	0.1116	0.9602	0.8525	0.8643	0.8438
0.1073	2.1581	2000	0.1096	0.9645	0.9117	0.8557	0.8684
0.0997	2.2660	2100	0.1022	0.9708	0.9292	0.8826	0.9014
0.089	2.3739	2200	0.1032	0.9691	0.9104	0.8785	0.8861
0.0688	2.4818	2300	0.1046	0.9652	0.9195	0.8446	0.8638
0.0894	2.5897	2400	0.0933	0.9727	0.9006	0.8957	0.8956
0.0691	2.6976	2500	0.0929	0.9714	0.9093	0.8807	0.8886
0.0903	2.8055	2600	0.1017	0.9666	0.9229	0.8679	0.8835
0.0582	2.9134	2700	0.1013	0.9670	0.9209	0.8649	0.8808

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