metadata

license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: SW2-DMAE-2
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6739130434782609

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