README.md

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-U3
    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-U3

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: 1.7136
• 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: 5.5e-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
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.86	3	7.9328	0.1087
No log	2.0	7	7.7596	0.1087
7.8559	2.86	10	7.3241	0.1087
7.8559	4.0	14	6.4450	0.1087
7.8559	4.86	17	5.7723	0.1087
6.3363	6.0	21	4.8772	0.1087
6.3363	6.86	24	4.2678	0.1087
6.3363	8.0	28	3.5000	0.1087
4.1887	8.86	31	2.9766	0.1087
4.1887	10.0	35	2.3876	0.1087
4.1887	10.86	38	2.0429	0.1087
2.602	12.0	42	1.7136	0.4565
2.602	12.86	45	1.5478	0.4565
2.602	14.0	49	1.3874	0.4565
1.6353	14.86	52	1.2968	0.4565
1.6353	16.0	56	1.2225	0.4565
1.6353	16.86	59	1.2071	0.4565
1.2533	18.0	63	1.2177	0.3261
1.2533	18.86	66	1.2197	0.3261
1.2088	20.0	70	1.2112	0.4565
1.2088	20.86	73	1.2101	0.4565
1.2088	22.0	77	1.2092	0.4565
1.1798	22.86	80	1.2081	0.4565
1.1798	24.0	84	1.2076	0.4565
1.1798	24.86	87	1.2049	0.4565
1.1825	26.0	91	1.2045	0.4565
1.1825	26.86	94	1.2029	0.4565
1.1825	28.0	98	1.2022	0.4565
1.1943	28.86	101	1.2014	0.4565
1.1943	30.0	105	1.2040	0.4565
1.1943	30.86	108	1.2050	0.4565
1.1772	32.0	112	1.2031	0.4565
1.1772	32.86	115	1.2019	0.4565
1.1772	34.0	119	1.2013	0.4565
1.1945	34.29	120	1.2012	0.4565

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0