In this repository I have uploaded implementations of some model deployment using flask, streamlit. (Will be updating it)
Streamlit API for datasets and models accuracy - https://streamlit-heroku-dataset-model.herokuapp.com/ (Currently not Active)
Resnet 50 is a convolutional neural network architecture that has been widely used for image classification tasks. It has 50 layers and has achieved state-of-the-art performance on various benchmark datasets.
Here are the steps to use Resnet 50 for an image classification task using Flask:
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Collect and preprocess data: You will need to collect a dataset of images for the classification task. The images may need to be preprocessed to ensure they are of the same size and resolution. You can use a library such as OpenCV or PIL to perform the preprocessing.
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Train the model: You can use an existing implementation of Resnet 50, such as the one provided by Keras, to train the model on your dataset. You can fine-tune the pre-trained model on your dataset, or train the model from scratch.
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Save the model: Once the model is trained, you can save it in a format such as HDF5 or SavedModel.
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Build a Flask app: You can use Flask to build a web application that will classify images using the trained Resnet 50 model. You can define a route that accepts an image file, preprocesses it, and passes it to the Resnet 50 model for classification.
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Deploy the app: You can deploy the Flask app to a server such as Heroku or AWS Elastic Beanstalk. Once the app is deployed, users can upload images to the app and receive a classification result.
In summary, using Resnet 50 for an image classification task using Flask involves collecting and preprocessing data, training the model, saving the model, building a Flask app, and deploying the app. With this setup, users can easily classify images using the Resnet 50 model through a web interface.