smids_3x_deit_base_sgd_001_fold3

hkivancoral/smids_3x_deit_base_sgd_001_fold3

smids_3x_deit_base_sgd_001_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

5% of all-time downloads

All-time downloads

190

Public

Repo size

17.5 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin343 MB · 100%

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: fdebd7da

Base models

facebook/deit-base-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_base_sgd_001_fold3

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: 0.2808
Accuracy: 0.8867

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
0.957	1.0	225	0.9484	0.6067
0.7319	2.0	450	0.7399	0.7417
0.5675	3.0	675	0.6023	0.8
0.5071	4.0	900	0.5170	0.8183
0.4486	5.0	1125	0.4641	0.8317
0.3985	6.0	1350	0.4275	0.85
0.3723	7.0	1575	0.4010	0.8483
0.3723	8.0	1800	0.3821	0.8533
0.3669	9.0	2025	0.3688	0.8617
0.3526	10.0	2250	0.3586	0.8667
0.3131	11.0	2475	0.3476	0.8733
0.3182	12.0	2700	0.3438	0.8717
0.329	13.0	2925	0.3317	0.875
0.2936	14.0	3150	0.3264	0.8733
0.281	15.0	3375	0.3211	0.875
0.29	16.0	3600	0.3177	0.875
0.3306	17.0	3825	0.3133	0.8767
0.268	18.0	4050	0.3109	0.875
0.2813	19.0	4275	0.3073	0.8817
0.29	20.0	4500	0.3041	0.8833
0.2543	21.0	4725	0.3034	0.8833
0.2485	22.0	4950	0.3036	0.8833
0.2389	23.0	5175	0.2976	0.885
0.2585	24.0	5400	0.2973	0.885
0.2265	25.0	5625	0.2954	0.8867
0.2806	26.0	5850	0.2939	0.8867
0.2023	27.0	6075	0.2926	0.8867
0.2462	28.0	6300	0.2897	0.885
0.2516	29.0	6525	0.2898	0.8867
0.2125	30.0	6750	0.2889	0.8867
0.2359	31.0	6975	0.2881	0.8883
0.2277	32.0	7200	0.2865	0.8867
0.2555	33.0	7425	0.2855	0.8883
0.202	34.0	7650	0.2847	0.8883
0.2329	35.0	7875	0.2859	0.8883
0.262	36.0	8100	0.2851	0.8883
0.2072	37.0	8325	0.2831	0.8867
0.2187	38.0	8550	0.2836	0.8867
0.2311	39.0	8775	0.2822	0.8867
0.2221	40.0	9000	0.2824	0.8867
0.2051	41.0	9225	0.2822	0.8867
0.2267	42.0	9450	0.2815	0.8867
0.2187	43.0	9675	0.2815	0.8867
0.2138	44.0	9900	0.2815	0.8867
0.2129	45.0	10125	0.2813	0.8867
0.2034	46.0	10350	0.2810	0.8867
0.2287	47.0	10575	0.2808	0.8867
0.2051	48.0	10800	0.2807	0.8867
0.256	49.0	11025	0.2807	0.8867
0.2173	50.0	11250	0.2808	0.8867

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 11, 2023
Updated: Dec 11, 2023