# Top 10 Pre-Trained Image Classification Models and How to Use Them

Are you looking to build an image classification model but don’t have the time, resources or expertise to train a deep neural network on your own dataset? Fear not, for pre-trained image classification models are here to save the day! These are state-of-the-art deep learning models that have been trained on large and diverse image datasets such as ImageNet, COCO, and PASCAL VOC, and are capable of recognizing various objects, animals, people, scenes, and more. In this article, we’ll introduce you to the top 10 pre-trained image classification models that you can use for your computer vision applications, and show you how to use them with popular deep learning frameworks such as TensorFlow and PyTorch.

## 1. VGG16

The VGG16 model is a classic and widely used pre-trained model for image classification. It was introduced by the Visual Geometry Group at the University of Oxford in 2014 and won the first place in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) that year. The VGG16 model consists of 16 layers of convolutional and fully connected neural networks, and can classify images into 1000 categories such as dog, cat, car, plane, etc. To use the VGG16 model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import VGG16
# Load the VGG16 pre-trained model
model = VGG16(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.vgg16.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.vgg16.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code will download the pre-trained weights of the VGG16 model from the internet, load them into memory, and apply the model to your input image (which should be in JPEG format and resized to (224, 224)). The output will be a list of (class_name, class_description, confidence_score) tuples, sorted by the highest confidence score first.

## 2. ResNet50

The ResNet50 model is another popular pre-trained model for image classification. It was introduced by Microsoft Research in 2015 and won the ILSVRC that year as well. The ResNet50 model consists of 50 layers of convolutional and fully connected neural networks, and is known for its residual connections that help alleviate the vanishing gradient problem. Similar to the VGG16 model, the ResNet50 model can classify images into 1000 categories. To use the ResNet50 model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import ResNet50
# Load the ResNet50 pre-trained model
model = ResNet50(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.resnet50.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.resnet50.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is very similar to the VGG16 example, except that we’re using the ResNet50 model instead. You can experiment with different pre-trained models and compare their performance and accuracy on your own datasets.

## 3. InceptionV3

The InceptionV3 model is a powerful and efficient pre-trained model for image classification. It was introduced by Google in 2015 and achieved the third place in the ILSVRC that year. The InceptionV3 model consists of an Inception architecture that uses multiple convolutions with different kernel sizes and pooling operations to capture different levels of abstraction. It can classify images into 1000 categories and is known for its high accuracy and speed. To use the InceptionV3 model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import InceptionV3
# Load the InceptionV3 pre-trained model
model = InceptionV3(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(299, 299))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.inception_v3.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.inception_v3.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is slightly different from the previous examples, as we’re using the InceptionV3 model that expects a larger image size of (299, 299) instead of (224, 224). We’re also using a different preprocessing function that is specific to the InceptionV3 model.

## 4. MobileNetV2

The MobileNetV2 model is a fast and lightweight pre-trained model for image classification. It was introduced by Google in 2018 and achieved the second place in the ILSVRC that year. The MobileNetV2 model consists of a mobile-size architecture that uses depthwise separable convolutions and linear bottlenecks to reduce the number of parameters and operations, while maintaining high accuracy. It can classify images into 1000 categories and is optimized for mobile and embedded devices that have limited computational resources. To use the MobileNetV2 model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
# Load the MobileNetV2 pre-trained model
model = MobileNetV2(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.mobilenet_v2.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is similar to the previous examples, but we’re using the MobileNetV2 model that expects an input image size of (224, 224) and a different preprocessing function that is specific to the MobileNetV2 model.

## 5. DenseNet121

The DenseNet121 model is a powerful and compact pre-trained model for image classification. It was introduced by the Facebook AI Research team in 2017 and achieved the first place in the ILSVRC that year. The DenseNet121 model consists of a densely connected convolutional neural network that uses feature reuse between layers to reduce the number of parameters and improve the gradient flow. It can classify images into 1000 categories and is known for its high accuracy and efficiency. To use the DenseNet121 model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import DenseNet121
# Load the DenseNet121 pre-trained model
model = DenseNet121(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.densenet.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.densenet.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is almost identical to the previous examples, except that we’re using the DenseNet121 model and its specific preprocessing and decoding functions.

## 6. Xception

The Xception model is another powerful and efficient pre-trained model for image classification. It was introduced by Google in 2017 and achieved the first place in the ILSVRC that year. The Xception model consists of an extreme inception architecture that replaces the standard convolutional layers with depthwise separable convolutions to reduce the number of parameters and increase the accuracy. It can classify images into 1000 categories and is known for its high accuracy and speed. To use the Xception model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import Xception
# Load the Xception pre-trained model
model = Xception(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(299, 299))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.xception.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.xception.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is similar to the InceptionV3 example, as the Xception model expects a larger input image size of (299, 299) and uses a different preprocessing function.

## 7. NASNetLarge

The NASNetLarge model is a state-of-the-art pre-trained model for image classification. It was introduced by Google in 2018 and achieved the first place in the ILSVRC that year. The NASNetLarge model consists of a neural architecture search (NAS) that uses reinforcement learning to discover neural network architectures for a given task. It can classify images into 1000 categories and is known for its high accuracy and complexity. To use the NASNetLarge model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import NASNetLarge
# Load the NASNetLarge pre-trained model
model = NASNetLarge(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(331, 331))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.nasnet.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.nasnet.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is similar to the Xception example, as the NASNetLarge model expects a larger input image size of (331, 331) and uses a different preprocessing and decoding function.

## 8. EfficientNetB0

The EfficientNetB0 model is a recent and promising pre-trained model for image classification. It was introduced by Google in 2019 and achieved state-of-the-art results on the ImageNet dataset with only 5.3 million parameters, which is significantly fewer than other models such as ResNet50 and InceptionV3. The EfficientNetB0 model uses a compound scaling method that increases the model depth, width, and resolution in a balanced way, and achieves high accuracy with low complexity. It can classify images into 1000 categories and is optimized for resource-constrained devices. To use the EfficientNetB0 model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import EfficientNetB0
# Load the EfficientNetB0 pre-trained model
model = EfficientNetB0(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(224, 224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.efficientnet.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.efficientnet.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is similar to the MobileNetV2 example, as the EfficientNetB0 model expects an input image size of (224, 224) and a specific preprocessing and decoding function.

## 9. SqueezeNet

The SqueezeNet model is a unique and lightweight pre-trained model for image classification. It was introduced by the University of California in 2016 and achieved high accuracy with only 1.2 million parameters, which is significantly fewer than other models such as VGG16 and ResNet50. The SqueezeNet model uses a Fire module that squeezes the input channels and expands the output channels to reduce the model size and improve the efficiency. It can classify images into 1000 categories and is optimized for low-memory and low-bandwidth devices. To use the SqueezeNet model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import SqueezeNet
# Load the SqueezeNet pre-trained model
model = SqueezeNet(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(227, 227))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.squeezenet.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.squeezenet.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is similar to the MobileNetV2 and EfficientNetB0 examples, as the SqueezeNet model expects an input image size of (227, 227) and a specific preprocessing and decoding function.

## 10. AlexNet

The AlexNet model is a classic and influential pre-trained model for image classification. It was introduced by the University of Toronto in 2012 and revitalized the deep learning research by demonstrating the power of convolutional neural networks on large-scale image datasets such as ImageNet. The AlexNet model consists of 8 layers of convolutional and fully connected neural networks, and can classify images into 1000 categories. To use the AlexNet model in TensorFlow, you can import it from the `keras.applications`

module, and apply it to your input image as follows:

```
import tensorflow as tf
from tensorflow.keras.applications import AlexNet
# Load the AlexNet pre-trained model
model = AlexNet(weights='imagenet', include_top=True)
# Load and preprocess your input image
image = tf.keras.preprocessing.image.load_img('image.jpg', target_size=(227, 227))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.keras.applications.alexnet.preprocess_input(image)
# Predict the class probabilities of your image
predictions = model.predict(image)
# Decode the predicted class labels
decode_predictions = tf.keras.applications.alexnet.decode_predictions(predictions, top=5)[0]
print(decode_predictions)
```

This code is similar to the SqueezeNet example, as the AlexNet model also expects an input image size of (227, 227) and a specific preprocessing and decoding function.

## Conclusion

Congratulations! You’ve just learned about the top 10 pre-trained image classification models that you can use for your computer vision projects, and how to use them with TensorFlow and other deep learning frameworks. These models are state-of-the-art deep neural networks that can recognize a wide range of objects, animals, people, scenes, and other visual entities, and can save you a lot of time and effort compared to training your own models from scratch. By experimenting with these models and fine-tuning them on your own datasets, you can create amazing computer vision applications that can improve people’s lives and businesses around the world. So what are you waiting for? Start exploring the exciting world of deep learning and pre-trained models today!

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