README.md

metadata

base_model: d071696/vit-finetune-scrap
tags:
  - image-classification
  - image-feature-extraction
  - image-to-text
  - generated_from_trainer
datasets:
  - arrow
metrics:
  - accuracy
model-index:
  - name: vit-finetune-scrap
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: d071696/scraps1
          type: arrow
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9260450160771704

vit-finetune-scrap

This model is a fine-tuned version of d071696/vit-finetune-scrap on the d071696/scraps1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3599
• Accuracy: 0.9260

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

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0021	3.22	1000	0.3599	0.9260

Framework versions

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