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
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2079 | 1.0 | 938 | 0.0193 | 0.996 | 0.9960 | 0.996 | 0.9960 | 0.996 | 0.996 | 0.9960 | 0.9960 | 0.996 | 0.9960 |
| 0.0154 | 2.0 | 1876 | 0.0068 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0018 | 3.0 | 2814 | 0.0014 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
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.