metadata

license: apache-2.0
base_model: NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: >-
      swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes
    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: 0.83

swin-tiny-patch4-window7-224-finetuned-student_six_classes-finetuned-student_six_classes

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

Loss: 0.4176
Accuracy: 0.83

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
No log	0.9231	3	0.4943	0.78
No log	1.8462	6	0.4716	0.78
No log	2.7692	9	0.4725	0.81
0.3732	4.0	13	0.4678	0.78
0.3732	4.9231	16	0.4779	0.78
0.3732	5.8462	19	0.4564	0.79
0.3459	6.7692	22	0.4556	0.82
0.3459	8.0	26	0.4757	0.77
0.3459	8.9231	29	0.4773	0.77
0.3273	9.8462	32	0.4661	0.77
0.3273	10.7692	35	0.4518	0.79
0.3273	12.0	39	0.4405	0.81
0.2974	12.9231	42	0.4359	0.82
0.2974	13.8462	45	0.4298	0.82
0.2974	14.7692	48	0.4242	0.84
0.2874	16.0	52	0.4199	0.84
0.2874	16.9231	55	0.4185	0.83
0.2874	17.8462	58	0.4179	0.83
0.2737	18.4615	60	0.4176	0.83

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1