google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May

Zannatul/google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May

google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May is a image classification model from Zannatul. 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 11, 2024
Updated: May 11, 2024
SHA: cccf3b97

Base models

Zannatul/final_withAug-google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24

Datasets

imagefolder

Model card

From the Hugging Face model README

google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May

This model is a fine-tuned version of Zannatul/final_withAug-google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24 on the bengali_food_images dataset. It achieves the following results on the evaluation set:

Loss: 0.0282
Accuracy: 0.9905

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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.269	0.09	100	0.4001	0.8723
0.1829	0.17	200	0.0928	0.9728
0.1737	0.26	300	0.1615	0.9457
0.2096	0.34	400	0.4138	0.9022
0.1855	0.43	500	0.1814	0.9511
0.0901	0.51	600	0.1435	0.9579
0.1406	0.6	700	0.1468	0.9620
0.136	0.68	800	0.1532	0.9565
0.0666	0.77	900	0.1177	0.9674
0.1145	0.85	1000	0.1794	0.9497
0.0865	0.94	1100	0.1113	0.9688
0.0612	1.02	1200	0.1270	0.9688
0.0038	1.11	1300	0.0724	0.9783
0.0006	1.19	1400	0.0715	0.9851
0.0007	1.28	1500	0.0616	0.9796
0.0579	1.36	1600	0.1259	0.9715
0.0009	1.45	1700	0.1028	0.9755
0.0295	1.53	1800	0.0637	0.9823
0.0484	1.62	1900	0.0893	0.9783
0.0371	1.71	2000	0.0637	0.9837
0.0359	1.79	2100	0.0389	0.9878
0.0006	1.88	2200	0.0750	0.9823
0.0189	1.96	2300	0.0451	0.9851
0.0442	2.05	2400	0.0772	0.9796
0.0006	2.13	2500	0.1988	0.9620
0.006	2.22	2600	0.0659	0.9864
0.0093	2.3	2700	0.0754	0.9810
0.0008	2.39	2800	0.0800	0.9783
0.0003	2.47	2900	0.0617	0.9864
0.0094	2.56	3000	0.0736	0.9837
0.0001	2.64	3100	0.0538	0.9823
0.001	2.73	3200	0.0606	0.9878
0.0001	2.81	3300	0.0433	0.9864
0.0001	2.9	3400	0.0583	0.9823
0.0001	2.98	3500	0.0388	0.9905
0.0001	3.07	3600	0.0408	0.9891
0.0001	3.15	3700	0.0375	0.9891
0.0001	3.24	3800	0.0367	0.9878
0.0001	3.32	3900	0.0355	0.9878
0.0001	3.41	4000	0.0395	0.9878
0.0001	3.5	4100	0.0382	0.9878
0.0001	3.58	4200	0.0399	0.9891
0.0001	3.67	4300	0.0396	0.9891
0.0072	3.75	4400	0.0355	0.9905
0.0001	3.84	4500	0.0284	0.9918
0.0001	3.92	4600	0.0282	0.9905

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 11, 2024
Updated: May 11, 2024