smids_1x_deit_tiny_rms_0001_fold3

hkivancoral/smids_1x_deit_tiny_rms_0001_fold3

smids_1x_deit_tiny_rms_0001_fold3 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. --

Downloads · 30 days

32% of all-time downloads

All-time downloads

Public

Parameters

5.5M

598 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors22.1 MB · 100%

At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Dec 6, 2023
Updated: Dec 6, 2023
SHA: 7c9766a1

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_1x_deit_tiny_rms_0001_fold3

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: 1.0984
Accuracy: 0.8817

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
0.7593	1.0	75	0.5650	0.795
0.4619	2.0	150	0.5773	0.74
0.3635	3.0	225	0.4403	0.85
0.3309	4.0	300	0.3368	0.865
0.2141	5.0	375	0.4183	0.86
0.1728	6.0	450	0.5130	0.8617
0.154	7.0	525	0.4040	0.8683
0.1331	8.0	600	0.5264	0.855
0.0922	9.0	675	0.7609	0.8467
0.0818	10.0	750	0.6611	0.8733
0.1085	11.0	825	0.6834	0.8783
0.0246	12.0	900	0.6979	0.8783
0.0878	13.0	975	0.9032	0.8433
0.1089	14.0	1050	0.7716	0.86
0.0143	15.0	1125	0.8328	0.8633
0.0139	16.0	1200	0.8299	0.86
0.0102	17.0	1275	0.9296	0.865
0.1127	18.0	1350	0.8373	0.8517
0.0195	19.0	1425	0.9545	0.87
0.0133	20.0	1500	1.0697	0.8617
0.0108	21.0	1575	1.2111	0.8333
0.0129	22.0	1650	1.0019	0.8667
0.0225	23.0	1725	1.0409	0.8567
0.0251	24.0	1800	1.1816	0.8467
0.0012	25.0	1875	1.1276	0.8633
0.003	26.0	1950	1.2613	0.8517
0.0173	27.0	2025	1.2463	0.8667
0.0413	28.0	2100	1.0810	0.8667
0.0002	29.0	2175	1.0875	0.8683
0.0257	30.0	2250	1.0511	0.86
0.0	31.0	2325	0.9064	0.895
0.0	32.0	2400	0.9226	0.8933
0.0	33.0	2475	0.9451	0.8933
0.0062	34.0	2550	0.9369	0.8917
0.0083	35.0	2625	0.9494	0.885
0.0047	36.0	2700	0.9547	0.885
0.0019	37.0	2775	0.9760	0.885
0.0	38.0	2850	1.0147	0.8883
0.0	39.0	2925	0.9604	0.885
0.0	40.0	3000	0.9768	0.8883
0.0	41.0	3075	0.9916	0.8817
0.003	42.0	3150	1.0329	0.8867
0.0028	43.0	3225	1.1065	0.8733
0.0	44.0	3300	1.0908	0.8817
0.0025	45.0	3375	1.0917	0.8817
0.0025	46.0	3450	1.0917	0.8817
0.0051	47.0	3525	1.0933	0.8817
0.0	48.0	3600	1.0955	0.8817
0.0	49.0	3675	1.0973	0.8817
0.0044	50.0	3750	1.0984	0.8817

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 6, 2023
Updated: Dec 6, 2023