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Augusto777/
SW2-DMAE-2

Name: SW2-DMAE-2
Availability: InStock

tensorboard

safetensors

swinv2

generated_from_trainer

imagefolder

microsoft/swinv2-tiny-patch4-window8-256

finetune

apache-2.0

model-index

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SW2-DMAE-2

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.0510
Accuracy: 0.6739

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: 1.5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	1.6269	0.1087
No log	2.0	7	1.6078	0.1087
1.618	2.86	10	1.5852	0.1087
1.618	4.0	14	1.5398	0.1087
1.618	4.86	17	1.4948	0.1087
1.5162	6.0	21	1.4344	0.1087
1.5162	6.86	24	1.3879	0.1087
1.5162	8.0	28	1.3288	0.1739
1.3459	8.86	31	1.2926	0.4565
1.3459	10.0	35	1.2562	0.4565
1.3459	10.86	38	1.2384	0.4565
1.2384	12.0	42	1.2205	0.4565
1.2384	12.86	45	1.2174	0.4565
1.2384	14.0	49	1.2131	0.4565
1.2049	14.86	52	1.2104	0.4565
1.2049	16.0	56	1.2086	0.4565
1.2049	16.86	59	1.2076	0.4565
1.1815	18.0	63	1.2052	0.4565
1.1815	18.86	66	1.2049	0.4565
1.1826	20.0	70	1.2019	0.4565
1.1826	20.86	73	1.1960	0.4565
1.1826	22.0	77	1.1927	0.4565
1.1647	22.86	80	1.1928	0.4565
1.1647	24.0	84	1.1924	0.4565
1.1647	24.86	87	1.1903	0.4565
1.1568	26.0	91	1.1879	0.4565
1.1568	26.86	94	1.1913	0.4565
1.1568	28.0	98	1.2046	0.4783
1.1432	28.86	101	1.1934	0.4783
1.1432	30.0	105	1.1665	0.4783
1.1432	30.86	108	1.1601	0.4783
1.1112	32.0	112	1.1624	0.5
1.1112	32.86	115	1.1664	0.5217
1.1112	34.0	119	1.1692	0.5
1.1132	34.86	122	1.1513	0.5435
1.1132	36.0	126	1.1384	0.5870
1.1132	36.86	129	1.1274	0.6087
1.0642	38.0	133	1.1443	0.5870
1.0642	38.86	136	1.1651	0.5
1.0439	40.0	140	1.1493	0.5
1.0439	40.86	143	1.1331	0.5217
1.0439	42.0	147	1.1032	0.5870
1.0362	42.86	150	1.0988	0.6304
1.0362	44.0	154	1.1093	0.5870
1.0362	44.86	157	1.1101	0.5870
1.0177	46.0	161	1.0903	0.6304
1.0177	46.86	164	1.0691	0.6522
1.0177	48.0	168	1.0510	0.6739
1.0	48.86	171	1.0451	0.6522
1.0	50.0	175	1.0425	0.6522
1.0	50.86	178	1.0512	0.6087
0.9636	52.0	182	1.0441	0.6304
0.9636	52.86	185	1.0402	0.6522
0.9636	54.0	189	1.0161	0.6522
0.9744	54.86	192	1.0073	0.6522
0.9744	56.0	196	1.0048	0.6522
0.9744	56.86	199	0.9993	0.6522
0.9233	58.0	203	0.9939	0.6522
0.9233	58.86	206	0.9939	0.6522
0.9452	60.0	210	0.9975	0.6522
0.9452	60.86	213	0.9981	0.6087
0.9452	62.0	217	0.9985	0.6087
0.9183	62.86	220	0.9969	0.6087
0.9183	64.0	224	0.9958	0.6304
0.9183	64.86	227	0.9928	0.6087
0.9449	66.0	231	0.9906	0.6087
0.9449	66.86	234	0.9893	0.6304
0.9449	68.0	238	0.9881	0.6522
0.9154	68.57	240	0.9880	0.6522

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0

Augusto777/SW2-DMAE-2

SW2-DMAE-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Augusto777/
SW2-DMAE-2