metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: segformer-class-classWeights-augmentation
    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: 0.9655172413793104
          - name: F1
            type: f1
            value: 0.964683592269799
          - name: Precision
            type: precision
            value: 0.9674329501915708
          - name: Recall
            type: recall
            value: 0.9655172413793104

segformer-class-classWeights-augmentation

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: 0.1453
Accuracy: 0.9655
F1: 0.9647
Precision: 0.9674
Recall: 0.9655

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: 10
eval_batch_size: 10
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.89	6	0.0454	1.0	1.0	1.0	1.0
0.1558	1.93	13	0.0816	0.9655	0.9647	0.9674	0.9655
0.1727	2.96	20	0.0775	0.9655	0.9647	0.9674	0.9655
0.1727	4.0	27	0.0443	0.9655	0.9647	0.9674	0.9655
0.1299	4.89	33	0.0535	0.9655	0.9647	0.9674	0.9655
0.1808	5.93	40	0.0298	0.9655	0.9647	0.9674	0.9655
0.1808	6.96	47	0.0195	1.0	1.0	1.0	1.0
0.1406	8.0	54	0.0526	0.9655	0.9647	0.9674	0.9655
0.1193	8.89	60	0.1453	0.9655	0.9647	0.9674	0.9655

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3