smids_5x_deit_base_rms_0001_fold4

hkivancoral/smids_5x_deit_base_rms_0001_fold4

smids_5x_deit_base_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 18, 2023
Updated: Dec 18, 2023
SHA: 6a217b3d

Base models

facebook/deit-base-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_5x_deit_base_rms_0001_fold4

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

Loss: 1.4272
Accuracy: 0.885

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
0.2738	1.0	375	0.3688	0.8617
0.164	2.0	750	0.4071	0.86
0.1224	3.0	1125	0.5148	0.87
0.1153	4.0	1500	0.5755	0.8833
0.0768	5.0	1875	0.5201	0.8733
0.0128	6.0	2250	0.6962	0.88
0.0434	7.0	2625	0.7802	0.8733
0.011	8.0	3000	0.8928	0.8567
0.0054	9.0	3375	0.8190	0.8583
0.0033	10.0	3750	0.8366	0.8683
0.0196	11.0	4125	0.7608	0.8817
0.0229	12.0	4500	0.8098	0.8683
0.032	13.0	4875	0.8776	0.865
0.0132	14.0	5250	0.9792	0.8517
0.0019	15.0	5625	0.8844	0.8783
0.0007	16.0	6000	0.9761	0.8683
0.0282	17.0	6375	0.8085	0.855
0.0269	18.0	6750	0.8221	0.875
0.0	19.0	7125	0.8222	0.8833
0.0006	20.0	7500	0.9491	0.8633
0.0061	21.0	7875	0.9907	0.86
0.0014	22.0	8250	1.0614	0.86
0.0136	23.0	8625	0.8637	0.8717
0.0038	24.0	9000	0.9073	0.8717
0.003	25.0	9375	1.0178	0.875
0.0311	26.0	9750	0.9666	0.8817
0.0228	27.0	10125	0.9904	0.8783
0.0	28.0	10500	1.1423	0.8617
0.0001	29.0	10875	1.1415	0.865
0.0142	30.0	11250	1.0575	0.88
0.0005	31.0	11625	1.2704	0.8683
0.0	32.0	12000	1.1747	0.875
0.0055	33.0	12375	1.1305	0.8717
0.0001	34.0	12750	1.1488	0.8817
0.0	35.0	13125	1.1227	0.8767
0.0	36.0	13500	1.1854	0.8733
0.0	37.0	13875	1.1866	0.8817
0.0066	38.0	14250	1.1780	0.885
0.0	39.0	14625	1.2908	0.88
0.0	40.0	15000	1.2885	0.8817
0.0	41.0	15375	1.3508	0.885
0.0	42.0	15750	1.3636	0.8817
0.0	43.0	16125	1.3897	0.8817
0.0	44.0	16500	1.3912	0.8783
0.0	45.0	16875	1.4003	0.885
0.0	46.0	17250	1.4104	0.885
0.0	47.0	17625	1.4228	0.885
0.0	48.0	18000	1.4261	0.885
0.0	49.0	18375	1.4274	0.885
0.0	50.0	18750	1.4272	0.885

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 18, 2023
Updated: Dec 18, 2023