sklearn resnet

Scikit-learn (sklearn) is a popular machine learning library in Python. However, it does not include a specific implementation of the ResNet architecture. ResNet stands for Residual Neural Network, which is a deep learning model known for its success in image classification tasks.

To use ResNet in sklearn, you would need to utilize a deep learning library such as TensorFlow or PyTorch, which provide pre-trained ResNet models. These libraries have implementations of ResNet that you can use for image classification tasks. You can then integrate these pre-trained models with sklearn for further analysis or predictions.

Here's an example of how you can use a pre-trained ResNet model from TensorFlow with sklearn:

import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from sklearn.preprocessing import StandardScaler

# Load the pre-trained ResNet model
model = ResNet50(weights='imagenet', include_top=False)

# Assuming you have some input data X
# Preprocess the data if required (e.g., resizing, normalization)
preprocessed_X = preprocess_data(X)

# Extract features using the ResNet model
features = model.predict(preprocessed_X)

# Scale the features
scaler = StandardScaler()
scaled_features = scaler.fit_transform(features)

# Perform further analysis or predictions using the scaled features

Remember to install the required libraries (e.g., tensorflow, scikit-learn) if you haven't already.