README.md

metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-fish
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9411764705882353

swin-tiny-patch4-window7-224-finetuned-fish

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.4264
• Accuracy: 0.9412

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 75

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.8	1	1.8035	0.2941
No log	1.6	2	1.7861	0.2941
No log	2.4	3	1.7554	0.2941
No log	4.0	5	1.6954	0.3529
No log	4.8	6	1.6780	0.4118
No log	5.6	7	1.6536	0.4118
No log	6.4	8	1.6222	0.4118
1.6467	8.0	10	1.4682	0.5294
1.6467	8.8	11	1.3261	0.5294
1.6467	9.6	12	1.1888	0.5294
1.6467	10.4	13	1.0433	0.5294
1.6467	12.0	15	0.8212	0.5882
1.6467	12.8	16	0.7240	0.7059
1.6467	13.6	17	0.6390	0.8235
1.6467	14.4	18	0.5594	0.8824
0.782	16.0	20	0.4647	0.8235
0.782	16.8	21	0.4264	0.9412
0.782	17.6	22	0.3983	0.9412
0.782	18.4	23	0.3760	0.9412
0.782	20.0	25	0.3751	0.8824
0.782	20.8	26	0.3553	0.8824
0.782	21.6	27	0.3161	0.8824
0.782	22.4	28	0.2706	0.9412
0.3228	24.0	30	0.2100	0.9412
0.3228	24.8	31	0.1885	0.9412
0.3228	25.6	32	0.1727	0.9412
0.3228	26.4	33	0.1818	0.9412
0.3228	28.0	35	0.1959	0.8824
0.3228	28.8	36	0.1889	0.9412
0.3228	29.6	37	0.1995	0.8824
0.3228	30.4	38	0.2093	0.8824
0.2375	32.0	40	0.1869	0.9412
0.2375	32.8	41	0.1648	0.9412
0.2375	33.6	42	0.1576	0.9412
0.2375	34.4	43	0.1709	0.9412
0.2375	36.0	45	0.1717	0.9412
0.2375	36.8	46	0.1783	0.9412
0.2375	37.6	47	0.1993	0.8824
0.2375	38.4	48	0.2085	0.8824
0.1897	40.0	50	0.2028	0.8824
0.1897	40.8	51	0.1704	0.9412
0.1897	41.6	52	0.1520	0.9412
0.1897	42.4	53	0.1325	0.9412
0.1897	44.0	55	0.1451	0.9412
0.1897	44.8	56	0.1664	0.9412
0.1897	45.6	57	0.1927	0.8824
0.1897	46.4	58	0.2202	0.8824
0.1676	48.0	60	0.2569	0.8824
0.1676	48.8	61	0.2748	0.8824
0.1676	49.6	62	0.2612	0.8824
0.1676	50.4	63	0.2414	0.8824
0.1676	52.0	65	0.1842	0.8824
0.1676	52.8	66	0.1597	0.8824
0.1676	53.6	67	0.1447	0.8824
0.1676	54.4	68	0.1359	0.9412
0.1452	56.0	70	0.1367	0.9412
0.1452	56.8	71	0.1402	0.9412
0.1452	57.6	72	0.1462	0.8824
0.1452	58.4	73	0.1515	0.8824
0.1452	60.0	75	0.1585	0.8824

Framework versions

• Transformers 4.40.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.15.0