smids_3x_deit_tiny_sgd_00001_fold4

hkivancoral/smids_3x_deit_tiny_sgd_00001_fold4

smids_3x_deit_tiny_sgd_00001_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. --

Downloads · 30 days

4% of all-time downloads

All-time downloads

557

Public

Repo size

1.1 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin22.2 MB · 100%

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: fdb790d3

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_3x_deit_tiny_sgd_00001_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: 1.1127
Accuracy: 0.3917

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: 1e-05
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
1.3668	1.0	225	1.3432	0.3467
1.2646	2.0	450	1.3216	0.3467
1.3414	3.0	675	1.3018	0.3417
1.249	4.0	900	1.2839	0.3417
1.3057	5.0	1125	1.2676	0.345
1.2751	6.0	1350	1.2530	0.35
1.2676	7.0	1575	1.2399	0.3483
1.1849	8.0	1800	1.2280	0.355
1.2324	9.0	2025	1.2173	0.365
1.2387	10.0	2250	1.2077	0.365
1.1505	11.0	2475	1.1990	0.3767
1.2698	12.0	2700	1.1911	0.37
1.1755	13.0	2925	1.1841	0.3717
1.1872	14.0	3150	1.1777	0.3717
1.2051	15.0	3375	1.1720	0.3683
1.1902	16.0	3600	1.1669	0.3733
1.144	17.0	3825	1.1622	0.38
1.0991	18.0	4050	1.1579	0.375
1.1454	19.0	4275	1.1539	0.38
1.1739	20.0	4500	1.1503	0.3717
1.193	21.0	4725	1.1470	0.375
1.154	22.0	4950	1.1439	0.3783
1.1465	23.0	5175	1.1410	0.3733
1.1397	24.0	5400	1.1384	0.3733
1.1433	25.0	5625	1.1360	0.375
1.1492	26.0	5850	1.1337	0.3717
1.1616	27.0	6075	1.1316	0.37
1.137	28.0	6300	1.1297	0.3767
1.1705	29.0	6525	1.1279	0.3817
1.1514	30.0	6750	1.1262	0.3817
1.1348	31.0	6975	1.1247	0.385
1.152	32.0	7200	1.1232	0.3817
1.1108	33.0	7425	1.1219	0.385
1.1032	34.0	7650	1.1207	0.3867
1.0973	35.0	7875	1.1195	0.3833
1.1492	36.0	8100	1.1185	0.3867
1.1046	37.0	8325	1.1176	0.3883
1.1281	38.0	8550	1.1167	0.3883
1.0952	39.0	8775	1.1160	0.3917
1.0675	40.0	9000	1.1153	0.3883
1.1354	41.0	9225	1.1147	0.39
1.1352	42.0	9450	1.1142	0.3883
1.1158	43.0	9675	1.1138	0.39
1.1146	44.0	9900	1.1135	0.39
1.0831	45.0	10125	1.1132	0.39
1.1243	46.0	10350	1.1130	0.39
1.0703	47.0	10575	1.1128	0.3917
1.1356	48.0	10800	1.1127	0.3917
1.1249	49.0	11025	1.1127	0.3917
1.0942	50.0	11250	1.1127	0.3917

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