smids_3x_deit_tiny_adamax_001_fold1

hkivancoral/smids_3x_deit_tiny_adamax_001_fold1

smids_3x_deit_tiny_adamax_001_fold1 is a image classification model from hkivancoral. 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: Dec 11, 2023
Updated: Dec 11, 2023
SHA: a5182c2c

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_tiny_adamax_001_fold1

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.1177
Accuracy: 0.8681

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5567	1.0	226	0.5713	0.7462
0.3828	2.0	452	0.3656	0.8564
0.3502	3.0	678	0.3812	0.8381
0.276	4.0	904	0.4744	0.8280
0.2886	5.0	1130	0.4090	0.8447
0.2624	6.0	1356	0.3974	0.8598
0.1626	7.0	1582	0.4030	0.8781
0.1074	8.0	1808	0.4449	0.8631
0.184	9.0	2034	0.3919	0.8831
0.0836	10.0	2260	0.5392	0.8564
0.0539	11.0	2486	0.6608	0.8381
0.0511	12.0	2712	0.6349	0.8564
0.046	13.0	2938	0.6675	0.8848
0.054	14.0	3164	0.6371	0.8664
0.0362	15.0	3390	0.8363	0.8414
0.0153	16.0	3616	0.7151	0.8664
0.0583	17.0	3842	0.7946	0.8514
0.0315	18.0	4068	0.8456	0.8614
0.0078	19.0	4294	0.7428	0.8431
0.0034	20.0	4520	0.8571	0.8614
0.0384	21.0	4746	0.7674	0.8731
0.0465	22.0	4972	0.6983	0.8715
0.0099	23.0	5198	1.0003	0.8581
0.0034	24.0	5424	0.9037	0.8631
0.0001	25.0	5650	0.9614	0.8548
0.0004	26.0	5876	1.0237	0.8548
0.0094	27.0	6102	0.8996	0.8698
0.0001	28.0	6328	0.9361	0.8765
0.0	29.0	6554	1.0528	0.8614
0.0001	30.0	6780	0.9933	0.8614
0.0001	31.0	7006	1.0600	0.8614
0.0	32.0	7232	1.0559	0.8664
0.0031	33.0	7458	1.0415	0.8598
0.0	34.0	7684	1.1002	0.8648
0.0	35.0	7910	1.0102	0.8731
0.0	36.0	8136	1.0422	0.8731
0.0	37.0	8362	1.0448	0.8681
0.0	38.0	8588	1.0235	0.8698
0.0	39.0	8814	1.0543	0.8648
0.0	40.0	9040	1.0808	0.8681
0.0029	41.0	9266	1.0840	0.8698
0.0032	42.0	9492	1.0669	0.8698
0.0	43.0	9718	1.1155	0.8631
0.0	44.0	9944	1.0986	0.8698
0.0	45.0	10170	1.1093	0.8664
0.0	46.0	10396	1.1032	0.8664
0.0	47.0	10622	1.1140	0.8664
0.0	48.0	10848	1.1176	0.8681
0.0	49.0	11074	1.1190	0.8681
0.0	50.0	11300	1.1177	0.8681

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 11, 2023
Updated: Dec 11, 2023