README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-finetune-scrap
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: d071696/scraps1
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 1

vit-finetune-scrap

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the d071696/scraps1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9895
• Accuracy: 1.0

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Framework versions

• Transformers 4.39.0
• Pytorch 2.2.1
• Datasets 2.18.0
• Tokenizers 0.15.2