rmsProps_VitB-p32-384-1e-4-batch_32_epoch_4_classes_24

ZaneHorrible/rmsProps_VitB-p32-384-1e-4-batch_32_epoch_4_classes_24

rmsProps_VitB-p32-384-1e-4-batch_32_epoch_4_classes_24 is a image classification model from ZaneHorrible. 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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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: May 15, 2024
Updated: May 15, 2024
SHA: da66deb3

Base models

google/vit-base-patch32-384

Datasets

imagefolder

Model card

From the Hugging Face model README

rmsProps_VitB-p32-384-1e-4-batch_32_epoch_4_classes_24

This model is a fine-tuned version of google/vit-base-patch32-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0970
Accuracy: 0.9770

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: 0.0001
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0292	0.14	100	0.1481	0.9655
0.0026	0.28	200	0.1295	0.9741
0.043	0.42	300	0.1905	0.9641
0.0099	0.56	400	0.2070	0.9641
0.0028	0.7	500	0.1291	0.9727
0.0206	0.84	600	0.1674	0.9612
0.0007	0.97	700	0.1307	0.9684
0.0004	1.11	800	0.1403	0.9698
0.0564	1.25	900	0.1523	0.9670
0.0247	1.39	1000	0.2492	0.9397
0.0005	1.53	1100	0.1087	0.9799
0.0172	1.67	1200	0.1220	0.9741
0.0073	1.81	1300	0.1126	0.9770
0.0009	1.95	1400	0.1326	0.9727
0.0101	2.09	1500	0.1339	0.9713
0.0018	2.23	1600	0.1344	0.9698
0.0002	2.37	1700	0.1588	0.9713
0.0147	2.51	1800	0.1543	0.9698
0.0292	2.65	1900	0.1266	0.9770
0.0001	2.79	2000	0.1535	0.9727
0.0	2.92	2100	0.1384	0.9756
0.0023	3.06	2200	0.1438	0.9713
0.0001	3.2	2300	0.1258	0.9741
0.0	3.34	2400	0.1038	0.9770
0.0001	3.48	2500	0.1010	0.9756
0.0001	3.62	2600	0.1007	0.9770
0.0	3.76	2700	0.1002	0.9770
0.0	3.9	2800	0.0970	0.9770

Framework versions

Transformers 4.39.3
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.15.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: May 15, 2024
Updated: May 15, 2024