smids_5x_deit_tiny_rms_0001_fold4

hkivancoral/smids_5x_deit_tiny_rms_0001_fold4

smids_5x_deit_tiny_rms_0001_fold4 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 16, 2023
Updated: Dec 16, 2023
SHA: efb8d6e4

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_5x_deit_tiny_rms_0001_fold4

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: 1.4357
Accuracy: 0.8667

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: 0.0001
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
0.3287	1.0	375	0.3863	0.85
0.2455	2.0	750	0.3649	0.8717
0.1213	3.0	1125	0.4642	0.8583
0.1727	4.0	1500	0.5805	0.8617
0.1128	5.0	1875	0.6371	0.8483
0.0689	6.0	2250	0.6331	0.8683
0.0983	7.0	2625	0.6829	0.865
0.1105	8.0	3000	0.6645	0.8617
0.0716	9.0	3375	0.9136	0.8583
0.0639	10.0	3750	0.7869	0.8867
0.0325	11.0	4125	0.8744	0.8733
0.0627	12.0	4500	0.9757	0.8567
0.0409	13.0	4875	0.9654	0.8633
0.0848	14.0	5250	0.8074	0.8667
0.0374	15.0	5625	0.9236	0.8667
0.037	16.0	6000	1.0898	0.8617
0.0497	17.0	6375	1.1236	0.8583
0.0095	18.0	6750	1.0183	0.87
0.0289	19.0	7125	1.0208	0.8783
0.0255	20.0	7500	1.1375	0.8667
0.0016	21.0	7875	1.1251	0.8617
0.0005	22.0	8250	1.0252	0.8717
0.015	23.0	8625	1.1223	0.865
0.0375	24.0	9000	1.0372	0.8733
0.0379	25.0	9375	0.9869	0.8667
0.0001	26.0	9750	1.0331	0.8733
0.0134	27.0	10125	0.9754	0.885
0.0	28.0	10500	1.0742	0.8583
0.0001	29.0	10875	1.0378	0.88
0.0	30.0	11250	1.1203	0.875
0.0077	31.0	11625	1.1471	0.8783
0.0003	32.0	12000	1.1437	0.8783
0.0	33.0	12375	1.1521	0.875
0.0003	34.0	12750	1.2362	0.865
0.0	35.0	13125	1.2535	0.8567
0.0	36.0	13500	1.2428	0.865
0.0002	37.0	13875	1.3504	0.8583
0.0191	38.0	14250	1.2705	0.87
0.0	39.0	14625	1.3466	0.8667
0.0	40.0	15000	1.3575	0.8617
0.0	41.0	15375	1.3681	0.8667
0.0	42.0	15750	1.3681	0.87
0.0	43.0	16125	1.3799	0.865
0.0	44.0	16500	1.3559	0.8667
0.0	45.0	16875	1.3770	0.865
0.0	46.0	17250	1.4044	0.8667
0.0	47.0	17625	1.4188	0.8683
0.0	48.0	18000	1.4286	0.8667
0.0	49.0	18375	1.4343	0.8667
0.0	50.0	18750	1.4357	0.8667

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 16, 2023
Updated: Dec 16, 2023