metadata

license: apache-2.0
base_model: WinKawaks/vit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: msi-vit-small-pretrain
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6394150417827298

msi-vit-small-pretrain

This model is a fine-tuned version of WinKawaks/vit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 2.4835
Accuracy: 0.6394

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0897	1.0	781	1.7652	0.6574
0.0539	2.0	1562	2.5512	0.6017
0.0127	3.0	2343	2.4835	0.6394

Framework versions

Transformers 4.36.0
Pytorch 2.0.1+cu117
Datasets 2.15.0
Tokenizers 0.15.0