smids_1x_deit_tiny_adamax_00001_fold2

hkivancoral/smids_1x_deit_tiny_adamax_00001_fold2

smids_1x_deit_tiny_adamax_00001_fold2 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 6, 2023
Updated: Dec 6, 2023
SHA: 88ba0e6b

Base models

facebook/deit-tiny-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

smids_1x_deit_tiny_adamax_00001_fold2

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: 0.9040
Accuracy: 0.8536

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
0.7328	1.0	75	0.6585	0.7321
0.4509	2.0	150	0.4910	0.7903
0.3653	3.0	225	0.4211	0.8253
0.2986	4.0	300	0.4104	0.8353
0.2809	5.0	375	0.3830	0.8336
0.2519	6.0	450	0.3604	0.8502
0.2227	7.0	525	0.3681	0.8552
0.2223	8.0	600	0.3795	0.8419
0.1548	9.0	675	0.3730	0.8552
0.1857	10.0	750	0.3841	0.8602
0.1291	11.0	825	0.3934	0.8602
0.08	12.0	900	0.4226	0.8552
0.0831	13.0	975	0.4456	0.8486
0.0574	14.0	1050	0.5173	0.8436
0.0548	15.0	1125	0.4816	0.8602
0.051	16.0	1200	0.5112	0.8569
0.0349	17.0	1275	0.5235	0.8536
0.0192	18.0	1350	0.5709	0.8502
0.027	19.0	1425	0.6300	0.8453
0.0409	20.0	1500	0.6458	0.8502
0.009	21.0	1575	0.6679	0.8552
0.0172	22.0	1650	0.6845	0.8519
0.0015	23.0	1725	0.7310	0.8552
0.0059	24.0	1800	0.7388	0.8552
0.0018	25.0	1875	0.7514	0.8569
0.0008	26.0	1950	0.7646	0.8552
0.0024	27.0	2025	0.7898	0.8569
0.0185	28.0	2100	0.7969	0.8519
0.0012	29.0	2175	0.8175	0.8619
0.003	30.0	2250	0.8189	0.8536
0.0009	31.0	2325	0.8193	0.8569
0.0003	32.0	2400	0.8343	0.8602
0.0006	33.0	2475	0.8317	0.8586
0.0003	34.0	2550	0.8413	0.8536
0.026	35.0	2625	0.8594	0.8519
0.0003	36.0	2700	0.8747	0.8519
0.0002	37.0	2775	0.8582	0.8536
0.0003	38.0	2850	0.8927	0.8536
0.0067	39.0	2925	0.8896	0.8519
0.0002	40.0	3000	0.8915	0.8536
0.003	41.0	3075	0.8737	0.8586
0.0003	42.0	3150	0.9065	0.8519
0.0159	43.0	3225	0.8958	0.8552
0.0007	44.0	3300	0.8969	0.8519
0.0002	45.0	3375	0.9007	0.8519
0.0002	46.0	3450	0.9037	0.8536
0.007	47.0	3525	0.9095	0.8536
0.0002	48.0	3600	0.9035	0.8536
0.0057	49.0	3675	0.9034	0.8536
0.0097	50.0	3750	0.9040	0.8536

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