metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-eurosat
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: images
          split: train
          args: images
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9609375

swin-tiny-patch4-window7-224-finetuned-eurosat

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: 0.1211
Accuracy: 0.9609

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: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	4	0.4862	0.8516
No log	2.0	8	0.4103	0.8828
0.4518	3.0	12	0.3210	0.8984
0.4518	4.0	16	0.2053	0.9375
0.2909	5.0	20	0.1675	0.9453
0.2909	6.0	24	0.1439	0.9531
0.2909	7.0	28	0.1448	0.9297
0.1492	8.0	32	0.1798	0.9531
0.1492	9.0	36	0.1360	0.9453
0.1161	10.0	40	0.1670	0.9531
0.1161	11.0	44	0.1637	0.9531
0.1161	12.0	48	0.1298	0.9531
0.1053	13.0	52	0.1162	0.9531
0.1053	14.0	56	0.1353	0.9531
0.0839	15.0	60	0.1211	0.9609
0.0839	16.0	64	0.1113	0.9609
0.0839	17.0	68	0.1145	0.9609
0.0689	18.0	72	0.1239	0.9531
0.0689	19.0	76	0.1280	0.9531
0.0581	20.0	80	0.1533	0.9531
0.0581	21.0	84	0.1323	0.9609
0.0581	22.0	88	0.1327	0.9531
0.0545	23.0	92	0.1529	0.9531
0.0545	24.0	96	0.1357	0.9531
0.046	25.0	100	0.1333	0.9531
0.046	26.0	104	0.1466	0.9531
0.046	27.0	108	0.1300	0.9531
0.0421	28.0	112	0.1077	0.9609
0.0421	29.0	116	0.0985	0.9609
0.0371	30.0	120	0.1186	0.9531
0.0371	31.0	124	0.1123	0.9531
0.0371	32.0	128	0.1144	0.9531
0.0348	33.0	132	0.1276	0.9531
0.0348	34.0	136	0.1488	0.9531
0.0211	35.0	140	0.1560	0.9531
0.0211	36.0	144	0.1477	0.9531
0.0211	37.0	148	0.1488	0.9531
0.0274	38.0	152	0.1467	0.9531
0.0274	39.0	156	0.1401	0.9531
0.0259	40.0	160	0.1379	0.9531

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3