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-landscape
    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: 0.9313077939233818

swin-tiny-patch4-window7-224-finetuned-landscape

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: 0.2104
Accuracy: 0.9313

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
5.3423	0.9684	23	2.8014	0.2867
0.9369	1.9789	47	0.3964	0.8758
0.4031	2.9895	71	0.2710	0.9168
0.3021	4.0	95	0.2255	0.9260
0.248	4.9684	118	0.2175	0.9300
0.2465	5.9789	142	0.2207	0.9207
0.1814	6.9895	166	0.2162	0.9207
0.1806	8.0	190	0.1954	0.9392
0.1839	8.9684	213	0.1863	0.9353
0.1699	9.9789	237	0.1898	0.9366
0.1493	10.9895	261	0.1981	0.9313
0.128	12.0	285	0.2112	0.9300
0.1473	12.9684	308	0.2174	0.9353
0.1304	13.9789	332	0.2083	0.9353
0.12	14.9895	356	0.2050	0.9379
0.0987	16.0	380	0.2054	0.9339
0.1187	16.9684	403	0.2136	0.9353
0.1187	17.9789	427	0.2136	0.9326
0.1325	18.9895	451	0.2089	0.9313
0.1071	19.3684	460	0.2104	0.9313

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1