vit-base-patch16-224-in21k_vegetables_clf – AI Model by DunnBC22 | AlphaNeural AI

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DunnBC22/
vit-base-patch16-224-in21k_vegetables_clf

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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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

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:

Sample Images

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.