File size: 3,307 Bytes
861e03a f3ac212 bcf9c1d f3ac212 e744f49 f3ac212 bcf9c1d 861e03a f3ac212 861e03a f3ac212 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
base_model: VGG16
library_name: tf-keras
license: mit
metrics:
- accuracy: 96.02%
- loss: 0.1585
tags:
- image-classification
- food
- fruit-recognition
- transfer-learning
model: vgg16-fruits-classifier
dataset: fruits-360
---
# VGG16 Fruits Classifier
## Model Description
This model is a fine-tuned version of the VGG16 architecture on the Fruits-360 dataset from Kaggle. VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford. The model achieves a high level of accuracy in recognizing 131 different types of fruits.
## Intended Uses & Limitations
### Intended Uses
- **Food Recognition**: This model can be used to classify images of fruits into one of 131 categories. It is particularly useful for applications in grocery stores, dietary monitoring, and educational tools about fruits.
### Limitations
- **Generalization**: The model is trained specifically on the Fruits-360 dataset and may not generalize well to fruits not included in this dataset or images with significantly different characteristics (e.g., different lighting, angles, or backgrounds).
- **Biases**: Performance may vary across different fruit categories due to the distribution and quality of the dataset images.
## Training and Evaluation Data
The model was trained and evaluated on the Fruits-360 dataset. This dataset contains over 70,000 images of fruits, split into training and test sets.
### Dataset Statistics
- **Training Set**: 54,190 images
- **Validation Set**: 13,502 images
- **Test Set**: 22,688 images
Each image is labeled with one of 131 fruit categories.
## Training Procedure
### Data Augmentation
- **Preprocessing**: All images were preprocessed using the `preprocess_input` function from the VGG16 module.
- **Augmentation**: Images were augmented with random rotations, shifts, zooms, flips, and more to improve the model's generalization.
### Model Architecture
The model architecture consists of the VGG16 base with the top fully connected layers removed. A global average pooling layer and a dense output layer with softmax activation were added.
### Hyperparameters
- **Optimizer**: Adam with a learning rate of 0.0001
- **Loss Function**: Categorical Crossentropy
- **Batch Size**: 64
- **Epochs**: 5
### Training Results
The model was trained for 5 epochs, achieving a validation accuracy of 97.04% and a test accuracy of 96.02%.
## Evaluation Metrics
- **Accuracy**: 96.02%
- **Loss**: 0.1585
These metrics indicate that the model performs very well on the test set of the Fruits-360 dataset.
## Framework Versions
- **TensorFlow**: 2.x
- **Keras**: 2.x
## Example Code
You can use this model with the following example code:
```python
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np
# Load the model
model = tf.keras.models.load_model('path_to_model')
# Load an image file that you want to classify
img_path = 'path_to_image'
img = image.load_img(img_path, target_size=(100, 100))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
# Predict the class of the image
predictions = model.predict(x)
predicted_class = np.argmax(predictions, axis=1)
print(f"Predicted class: {predicted_class}") |