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---
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 |