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

Loss: 0.0404
Accuracy: 0.9925
Precision: 0.9926
Recall: 0.9925
F1: 0.9925

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0492	0.7692	50	0.1117	0.9774	0.9776	0.9774	0.9774
0.0432	1.5385	100	0.1428	0.9624	0.9650	0.9624	0.9620
0.0119	2.3077	150	0.0736	0.9850	0.9851	0.9850	0.9850
0.0038	3.0769	200	0.0404	0.9925	0.9926	0.9925	0.9925
0.0046	3.8462	250	0.0472	0.9850	0.9856	0.9850	0.9850

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

dzinampini/beans-leaf-disease-detection

beans-leaf-disease-detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dzinampini/
beans-leaf-disease-detection