ViT_flower102

KietZer0/ViT_flower102

ViT_flower102 is a image classification model from KietZer0. 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. --

Downloads · 30 days

5% of all-time downloads

All-time downloads

183

Public

Parameters

85.9M

30.6 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors344 MB · 100%

At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Apr 3, 2024
Updated: Apr 3, 2024
SHA: dd446157

Base models

google/vit-base-patch16-224-in21k

Model card

From the Hugging Face model README

ViT_Flower102

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: 0.0587
Accuracy: 0.9853
Precision: 0.9853
Recall: 0.9853
F1: 0.9853

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: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.3962	0.22	100	1.7861	0.7373	0.7373	0.7373	0.7373
0.5196	0.45	200	0.7527	0.8951	0.8951	0.8951	0.8951
0.355	0.67	300	0.3937	0.9451	0.9451	0.9451	0.9451
0.1966	0.89	400	0.3312	0.9422	0.9422	0.9422	0.9422
0.1262	1.11	500	0.2145	0.9608	0.9608	0.9608	0.9608
0.1512	1.34	600	0.1652	0.9706	0.9706	0.9706	0.9706
0.1414	1.56	700	0.2562	0.9471	0.9471	0.9471	0.9471
0.1235	1.78	800	0.1742	0.9657	0.9657	0.9657	0.9657
0.0428	2.0	900	0.1809	0.9578	0.9578	0.9578	0.9578
0.0202	2.23	1000	0.1518	0.9637	0.9637	0.9637	0.9637
0.0451	2.45	1100	0.1214	0.9725	0.9725	0.9725	0.9725
0.0208	2.67	1200	0.1274	0.9725	0.9725	0.9725	0.9725
0.0673	2.9	1300	0.1904	0.9627	0.9627	0.9627	0.9627
0.0347	3.12	1400	0.1101	0.9765	0.9765	0.9765	0.9765
0.0035	3.34	1500	0.1274	0.9765	0.9765	0.9765	0.9765
0.0629	3.56	1600	0.0743	0.9833	0.9833	0.9833	0.9833
0.0368	3.79	1700	0.0801	0.9804	0.9804	0.9804	0.9804
0.0021	4.01	1800	0.0947	0.9794	0.9794	0.9794	0.9794
0.0037	4.23	1900	0.0990	0.9775	0.9775	0.9775	0.9775
0.0015	4.45	2000	0.0782	0.9824	0.9824	0.9824	0.9824
0.002	4.68	2100	0.0924	0.9775	0.9775	0.9775	0.9775
0.0013	4.9	2200	0.0648	0.9892	0.9892	0.9892	0.9892
0.0013	5.12	2300	0.0705	0.9843	0.9843	0.9843	0.9843
0.0012	5.35	2400	0.0667	0.9853	0.9853	0.9853	0.9853
0.0011	5.57	2500	0.0654	0.9863	0.9863	0.9863	0.9863
0.001	5.79	2600	0.0809	0.9804	0.9804	0.9804	0.9804
0.001	6.01	2700	0.0603	0.9853	0.9853	0.9853	0.9853
0.0009	6.24	2800	0.0587	0.9853	0.9853	0.9853	0.9853
0.0009	6.46	2900	0.0613	0.9863	0.9863	0.9863	0.9863
0.0354	6.68	3000	0.0625	0.9863	0.9863	0.9863	0.9863
0.0009	6.9	3100	0.0640	0.9863	0.9863	0.9863	0.9863
0.0009	7.13	3200	0.0637	0.9863	0.9863	0.9863	0.9863
0.0008	7.35	3300	0.0640	0.9863	0.9863	0.9863	0.9863
0.0007	7.57	3400	0.0638	0.9863	0.9863	0.9863	0.9863
0.0008	7.8	3500	0.0632	0.9863	0.9863	0.9863	0.9863
0.0006	8.02	3600	0.0628	0.9863	0.9863	0.9863	0.9863
0.0006	8.24	3700	0.0628	0.9863	0.9863	0.9863	0.9863
0.0007	8.46	3800	0.0629	0.9863	0.9863	0.9863	0.9863
0.0006	8.69	3900	0.0628	0.9863	0.9863	0.9863	0.9863
0.0007	8.91	4000	0.0629	0.9863	0.9863	0.9863	0.9863
0.0006	9.13	4100	0.0629	0.9863	0.9863	0.9863	0.9863
0.0005	9.35	4200	0.0629	0.9863	0.9863	0.9863	0.9863
0.0006	9.58	4300	0.0629	0.9863	0.9863	0.9863	0.9863
0.0006	9.8	4400	0.0629	0.9863	0.9863	0.9863	0.9863

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Apr 3, 2024
Updated: Apr 3, 2024