exper3_mesum5

sudo-s/exper3_mesum5

exper3_mesum5 is a image classification model from sudo-s. 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: Jul 22, 2022
Updated: Jul 22, 2022
SHA: cb127fb5

Model card

From the Hugging Face model README

exper3_mesum5

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set:

Loss: 0.6366
Accuracy: 0.8367

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: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.895	0.23	100	3.8276	0.1935
3.1174	0.47	200	3.1217	0.3107
2.6	0.7	300	2.5399	0.4207
2.256	0.93	400	2.1767	0.5160
1.5441	1.16	500	1.8086	0.5852
1.3834	1.4	600	1.5565	0.6325
1.1995	1.63	700	1.3339	0.6763
1.0845	1.86	800	1.3299	0.6533
0.6472	2.09	900	1.0679	0.7219
0.5948	2.33	1000	1.0286	0.7124
0.5565	2.56	1100	0.9595	0.7284
0.4879	2.79	1200	0.8915	0.7420
0.2816	3.02	1300	0.8159	0.7763
0.2412	3.26	1400	0.7766	0.7911
0.2015	3.49	1500	0.7850	0.7828
0.274	3.72	1600	0.7361	0.7935
0.1244	3.95	1700	0.7299	0.7911
0.0794	4.19	1800	0.7441	0.7846
0.0915	4.42	1900	0.7614	0.7941
0.0817	4.65	2000	0.7310	0.8012
0.0561	4.88	2100	0.7222	0.8065
0.0165	5.12	2200	0.7515	0.8059
0.0168	5.35	2300	0.6687	0.8213
0.0212	5.58	2400	0.6671	0.8249
0.0389	5.81	2500	0.6893	0.8278
0.0087	6.05	2600	0.6839	0.8260
0.0087	6.28	2700	0.6412	0.8320
0.0077	6.51	2800	0.6366	0.8367
0.0065	6.74	2900	0.6697	0.8272
0.0061	6.98	3000	0.6510	0.8349
0.0185	7.21	3100	0.6452	0.8367
0.0059	7.44	3200	0.6426	0.8379
0.0062	7.67	3300	0.6398	0.8379
0.0315	7.91	3400	0.6397	0.8385

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Jul 22, 2022
Updated: Jul 22, 2022