DunnBC22/vit-base-patch16-224-in21k-Mango_leaf_Disease

vit-base-patch16-224-in21k-MangoleafDisease

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

• —Loss: 0.0189
• —Accuracy: 1.0
• —Weighted f1: 1.0
• —Micro f1: 1.0
• —Macro f1: 1.0
• —Weighted recall: 1.0
• —Micro recall: 1.0
• —Macro recall: 1.0
• —Weighted precision: 1.0
• —Micro precision: 1.0
• —Macro precision: 1.0

Model description

This is a multiclass image classification model of mango leaf diseases.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/VisionAudioandMultimodalProjects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Mango%20Leaf%20Disease%20Dataset/MangoLeafDisease_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/aryashah2k/mango-leaf-disease-dataset

Sample Images From Dataset:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0002
• —trainbatchsize: 16
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 2

Training results

Framework versions

• —Transformers 4.27.4
• —Pytorch 2.0.0
• —Datasets 2.11.0
• —Tokenizers 0.13.3

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.