---
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- computer vision
- flower
- image classification
- resnet50
---


# flower_image_classification_ResNet50_v1.0 

This model is a fine-tuned version of Keras ResNet50 on the tf_flower dataset (https://www.tensorflow.org/datasets/catalog/tf_flowers).
It achieves the following results on the evaluation set:
- Loss: 0.7941
- Accuracy: 0.8571

## Model description

A slightly customized image classification model for classify 5 labels of flowers ('daisy', 'dandelion', 'roses', 'sunflowers', 'tulips')

## Intended uses & limitations

This model is fined tune solely for flower image classification. 

## Training and evaluation data

Training and testing data is splitted into 80:20 portion.
Total data       : 3670 files belonging to 5 classes
Training data    : 2753 files (80%)
Validation data  : 917 files  (20%)


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-03
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Adam 
- loss: categorical_crossentropy
- num_epochs: 5

### Fine-Tuning Results

| Epoch | Step  | Training Loss   | Training Accuracy | Validation Loss | Validation Accuracy|
|:-----:|:-----:|:---------------:|:-----------------:|:---------------:|:------------------:|
| 1.0   | 345   | 13.9143         | 0.6478            | 0.5310          | 0.8288             |
| 2.0   | 690   | 0.2639          | 0.9161            | 0.6046          | 0.8419             |
| 3.0   | 1035  | 0.1369          | 0.9539            | 0.5483          | 0.8561             |
| 4.0   | 1380  | 0.0863          | 0.9703            | 0.5699          | 0.8659             |
| 5.0   | 1725  | 0.0686          | 0.9837            | 0.7941          | 0.8571             |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0
- opencv-contrib-python-4.10.0.82