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-ex
    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: 0.45652173913043476

swinv2-tiny-patch4-window8-256-DMAE-ex

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: 11.3982
Accuracy: 0.4565

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: 0.1
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	45.5320	0.3261
No log	2.0	7	11.3982	0.4565
37.3882	2.86	10	14.6592	0.3261
37.3882	4.0	14	5.4321	0.4565
37.3882	4.86	17	2.1913	0.1087
7.8109	6.0	21	7.5738	0.1087
7.8109	6.86	24	8.5702	0.4565
7.8109	8.0	28	5.5301	0.1087
6.7711	8.86	31	2.6876	0.4565
6.7711	10.0	35	1.8742	0.1087
6.7711	10.86	38	1.5266	0.4565
1.7995	12.0	42	1.5311	0.1087
1.7995	12.86	45	1.4439	0.4565
1.7995	14.0	49	1.2218	0.4565
1.5366	14.86	52	1.3226	0.4565
1.5366	16.0	56	1.6288	0.1087
1.5366	16.86	59	1.7526	0.4565
1.5748	18.0	63	1.3699	0.3261
1.5748	18.86	66	1.2663	0.4565
1.3933	20.0	70	1.2222	0.4565
1.3933	20.86	73	1.2388	0.3261
1.3933	22.0	77	1.2831	0.4565
1.2788	22.86	80	1.2515	0.3261
1.2788	24.0	84	1.2105	0.4565
1.2788	24.86	87	1.2141	0.4565
1.2218	26.0	91	1.2215	0.4565
1.2218	26.86	94	1.2189	0.4565
1.2218	28.0	98	1.2102	0.4565
1.2039	28.86	101	1.2094	0.4565
1.2039	30.0	105	1.2065	0.4565
1.2039	30.86	108	1.2125	0.4565
1.2131	32.0	112	1.2107	0.4565
1.2131	32.86	115	1.2078	0.4565
1.2131	34.0	119	1.2068	0.4565
1.211	34.29	120	1.2067	0.4565

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0