smids_3x_deit_tiny_sgd_001_fold1

hkivancoral/smids_3x_deit_tiny_sgd_001_fold1

smids_3x_deit_tiny_sgd_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 12, 2023
Updated: Dec 12, 2023
SHA: 1e85bb59

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_tiny_sgd_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: 0.3078
Accuracy: 0.8731

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
0.9352	1.0	226	0.9208	0.5376
0.6928	2.0	452	0.7389	0.6861
0.5623	3.0	678	0.6105	0.7429
0.5246	4.0	904	0.5485	0.7563
0.5426	5.0	1130	0.4979	0.7880
0.4977	6.0	1356	0.4581	0.8080
0.3766	7.0	1582	0.4327	0.8130
0.4038	8.0	1808	0.4167	0.8097
0.3541	9.0	2034	0.3987	0.8431
0.3195	10.0	2260	0.3857	0.8247
0.3215	11.0	2486	0.3815	0.8297
0.2707	12.0	2712	0.3604	0.8414
0.2756	13.0	2938	0.3575	0.8364
0.2853	14.0	3164	0.3492	0.8414
0.3202	15.0	3390	0.3434	0.8447
0.3213	16.0	3616	0.3398	0.8497
0.246	17.0	3842	0.3305	0.8581
0.2485	18.0	4068	0.3288	0.8564
0.2691	19.0	4294	0.3315	0.8598
0.2123	20.0	4520	0.3213	0.8648
0.2607	21.0	4746	0.3252	0.8564
0.2646	22.0	4972	0.3186	0.8664
0.2851	23.0	5198	0.3202	0.8631
0.2373	24.0	5424	0.3144	0.8748
0.1908	25.0	5650	0.3143	0.8698
0.2924	26.0	5876	0.3120	0.8698
0.1662	27.0	6102	0.3113	0.8748
0.2215	28.0	6328	0.3120	0.8681
0.1838	29.0	6554	0.3136	0.8698
0.2131	30.0	6780	0.3140	0.8731
0.2074	31.0	7006	0.3100	0.8715
0.194	32.0	7232	0.3083	0.8748
0.1635	33.0	7458	0.3091	0.8748
0.1521	34.0	7684	0.3083	0.8748
0.2333	35.0	7910	0.3078	0.8748
0.1942	36.0	8136	0.3076	0.8731
0.242	37.0	8362	0.3062	0.8748
0.2131	38.0	8588	0.3090	0.8748
0.2044	39.0	8814	0.3079	0.8748
0.1565	40.0	9040	0.3082	0.8731
0.1709	41.0	9266	0.3089	0.8748
0.2023	42.0	9492	0.3080	0.8748
0.2299	43.0	9718	0.3077	0.8731
0.1365	44.0	9944	0.3081	0.8765
0.1955	45.0	10170	0.3078	0.8748
0.2025	46.0	10396	0.3089	0.8781
0.1982	47.0	10622	0.3076	0.8731
0.1881	48.0	10848	0.3078	0.8731
0.1389	49.0	11074	0.3077	0.8731
0.1646	50.0	11300	0.3078	0.8731

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 12, 2023
Updated: Dec 12, 2023