metadata

library_name: transformers
license: apache-2.0
base_model: microsoft/swin-small-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-small-patch4-window7-224-finetuned-eurosat
    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: 0.8471810089020771

swin-small-patch4-window7-224-finetuned-eurosat

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

Loss: 0.5541
Accuracy: 0.8472

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.8572	1.0	48	2.6775	0.2226
1.6357	2.0	96	1.3411	0.5519
1.076	3.0	144	0.9200	0.6973
1.0091	4.0	192	0.8266	0.7062
0.777	5.0	240	0.6904	0.7685
0.6453	6.0	288	0.6149	0.7864
0.6124	7.0	336	0.6207	0.7923
0.5204	8.0	384	0.6130	0.7908
0.529	9.0	432	0.6334	0.8042
0.4394	10.0	480	0.5370	0.8249
0.4398	11.0	528	0.5589	0.8249
0.3996	12.0	576	0.5391	0.8501
0.3585	13.0	624	0.5796	0.8205
0.3276	14.0	672	0.5851	0.8338
0.3382	15.0	720	0.5508	0.8457
0.3212	16.0	768	0.5279	0.8605
0.3226	17.0	816	0.5769	0.8338
0.2836	18.0	864	0.5942	0.8294
0.2743	19.0	912	0.5862	0.8309
0.2637	20.0	960	0.5586	0.8234
0.2567	21.0	1008	0.5335	0.8427
0.2932	22.0	1056	0.5653	0.8383
0.2532	23.0	1104	0.5493	0.8368
0.2286	24.0	1152	0.5798	0.8383
0.206	25.0	1200	0.5623	0.8487
0.2288	26.0	1248	0.5566	0.8442
0.2059	27.0	1296	0.5437	0.8457
0.1904	28.0	1344	0.5500	0.8338
0.2416	29.0	1392	0.5563	0.8487
0.1967	29.3789	1410	0.5541	0.8472

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1