README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - vision
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: outputs
    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: 0.8571428571428571

Cowboy Hat emoji 🤠 (Western)

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

• Loss: 0.5372
• Accuracy: 0.8571

Model description

When you want to know if an art is 🤠 or not 🤠.

Intended uses & limitations

filter gelbooru data on 🤠 or not 🤠

Training and evaluation data

Selected 72 images of 🤠 and not 🤠.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• 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: 3.0

Training results

Works OK. Needs more finetuning.

Framework versions

• Transformers 4.30.0.dev0
• Pytorch 1.13.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3