vit-base-patch16-224-in21k_vegetables_clf

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

• Loss: 0.0014
• Accuracy: 1.0
• F1
  • Weighted: 1.0
  • Micro: 1.0
  • Macro: 1.0
• Recall
  • Weighted: 1.0
  • Micro: 1.0
  • Macro: 1.0
• Precision
  • Weighted: 1.0
  • Micro: 1.0
  • Macro: 1.0

Model description

This is a multiclass image classification model of different vegetables.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Vegetable%20Image%20Classification/Vegetables_ViT.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset

Sample Images From Dataset:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 16
• 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

Training results

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.1
• Datasets 2.8.0
• Tokenizers 0.12.1

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.