README.md

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: 0.1855
• Accuracy: 0.9655
• F1: 0.9647
• Precision: 0.9674
• Recall: 0.9655
• Learning Rate: 0.0000

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	Rate
No log	0.89	6	0.1113	0.9655	0.9647	0.9674	0.9655	0.0000
0.1153	1.93	13	0.0929	0.9655	0.9647	0.9674	0.9655	0.0000
0.2246	2.96	20	0.1026	0.9655	0.9647	0.9674	0.9655	0.0000
0.2246	4.0	27	0.0391	0.9655	0.9647	0.9674	0.9655	0.0000
0.1433	4.89	33	0.0673	0.9655	0.9647	0.9674	0.9655	0.0000
0.1816	5.93	40	0.0794	0.9655	0.9647	0.9674	0.9655	0.0000
0.1816	6.96	47	0.0687	0.9655	0.9647	0.9674	0.9655	0.0000
0.1448	8.0	54	0.1123	0.9655	0.9647	0.9674	0.9655	0.0000
0.1124	8.89	60	0.1855	0.9655	0.9647	0.9674	0.9655	0.0000

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3