metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: fruits-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: Schram03/fruits
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9280373831775701
fruits-classification
This model is a fine-tuned version of google/vit-base-patch16-224 on the augmented Schram03/fruits dataset. It achieves the following results on the evaluation set:
- Loss: 0.3039
- Accuracy: 0.9280
Model description
This model classifies the 33 different types of fruit in the Schram03/fruits dataset.
Intended uses & limitations
With further developement it could be possible to differentiat foul fruits or specific subtypes of fruit. This however would need an extensive Dataset. Currently the model is able to differentiat the trained 33 types of fruit. However, there are limitations especially when using similar fruits like peaches and nectarines.
Training and evaluation data
80 % Train, 20 % Evaluation
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5816 | 1.0 | 1070 | 0.5238 | 0.8887 |
| 0.3761 | 2.0 | 2140 | 0.3568 | 0.9182 |
| 0.2934 | 3.0 | 3210 | 0.2852 | 0.9322 |
| 0.2719 | 4.0 | 4280 | 0.2681 | 0.9388 |
| 0.253 | 5.0 | 5350 | 0.2705 | 0.9388 |
Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1