metadata

license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window8-256-DMAE-U
    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: 0.6521739130434783

swinv2-tiny-patch4-window8-256-DMAE-U

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: 1.0462
Accuracy: 0.6522

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: 4e-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: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	1.3654	0.4783
No log	2.0	7	1.3027	0.4565
1.3356	2.86	10	1.2580	0.4565
1.3356	4.0	14	1.2157	0.4565
1.3356	4.86	17	1.2121	0.4565
1.202	6.0	21	1.2014	0.4565
1.202	6.86	24	1.2013	0.4565
1.202	8.0	28	1.1949	0.4565
1.1884	8.86	31	1.1934	0.4565
1.1884	10.0	35	1.1916	0.4565
1.1884	10.86	38	1.1829	0.4565
1.1351	12.0	42	1.1568	0.4565
1.1351	12.86	45	1.1371	0.4565
1.1351	14.0	49	1.1238	0.4783
1.132	14.86	52	1.1183	0.5217
1.132	16.0	56	1.0962	0.6087
1.132	16.86	59	1.0737	0.6087
1.0659	18.0	63	1.0462	0.6522
1.0659	18.86	66	1.0217	0.6304
1.0299	20.0	70	0.9955	0.6522
1.0299	20.86	73	0.9767	0.6304
1.0299	22.0	77	0.9495	0.6304
0.9684	22.86	80	0.9328	0.6304
0.9684	24.0	84	0.9176	0.6304
0.9684	24.86	87	0.9078	0.6304
0.9301	26.0	91	0.8966	0.6304
0.9301	26.86	94	0.8951	0.6304
0.9301	28.0	98	0.8894	0.6522
0.9258	28.86	101	0.8820	0.6304
0.9258	30.0	105	0.8771	0.6304
0.9258	30.86	108	0.8776	0.6522
0.8877	32.0	112	0.8754	0.6522
0.8877	32.86	115	0.8732	0.6522
0.8877	34.0	119	0.8721	0.6522
0.8953	34.29	120	0.8719	0.6522

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0