| About this DeepLearning Model: |
| We will build an front end application to upload the image and get the deeplearning model predicts the name of the object with acccuracy. |
|
|
| Steps for building the Image classification model: |
| 1. Image classification model using pretrained DL model |
| 1.1 Define deeplearning model |
| 2.2 Preprocess the data |
| 3.3 Get prediction |
|
|
| 1.1 Define deep learning model |
| # import required modules |
| import json |
| import numpy as np |
| from PIL import Image |
| import matplotlib.pyplot as plt |
|
|
| # import pytorch related modules |
| import torch |
| from torchvision import transforms |
| from torchvision.models import densenet121 |
| # define pretrained DL model |
| model = densenet121(pretrained=True) |
|
|
| model.eval(); |
| 1.2 Preprocess data |
| # load image using PIL |
| input_image = Image.open(filename) |
| |
| # preprocess image according to the pretrained model |
| preprocess = transforms.Compose([ |
| transforms.Resize(256), |
| transforms.CenterCrop(224), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
| ]) |
| input_tensor = preprocess(input_image) |
| |
| # create a mini-batch as expected by the model |
| input_batch = input_tensor.unsqueeze(0) |
| |
| # pass input batch to the model |
| with torch.no_grad(): |
| output = model(input_batch) |
| 1.3 Get prediction |
| pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy() |
| np.argmax(pred) |
| # download classes on which the model was trained on |
| !wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json |
| # get the prediction accuracy |
| print(classes[str(np.argmax(pred))][1], round(max(pred)*100, 2)) |
| 2. Deploying Image Classification model |
| 1.1 Install required libraries |
| 1.2 Setup DL model using streamlit |
| 1.3 Deploy DL model on AWS/Colab/HF spaces |
| |
| 1.1 Install required libraries |
| !pip install -q streamlit |
| !pip install -q pyngrok |
| 1.2 Setup DL model using streamlit |
| %%writefile app.py |
|
|
| ## create streamlit app |
|
|
| # import required libraries and modules |
| import json |
| import numpy as np |
| import matplotlib.pyplot as plt |
|
|
| import torch |
| from PIL import Image |
| from torchvision import transforms |
| from torchvision.models import densenet121 |
|
|
| import streamlit as st |
|
|
| # define prediction function |
| def predict(image): |
| # load DL model |
| model = densenet121(pretrained=True) |
| |
| model.eval() |
| |
| # load classes |
| with open('imagenet_class_index.json', 'r') as f: |
| classes = json.load(f) |
| |
| # preprocess image |
| preprocess = transforms.Compose([ |
| transforms.Resize(256), |
| transforms.CenterCrop(224), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), |
| ]) |
| input_tensor = preprocess(input_image) |
| input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model |
| |
| # get prediction |
| with torch.no_grad(): |
| output = model(input_batch) |
| |
| pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy() |
| |
| # return confidence and label |
| confidence = round(max(pred)*100, 2) |
| label = classes[str(np.argmax(pred))][1] |
| |
| return confidence, label |
| |
| # define image file uploader |
| image = st.file_uploader("Upload image here") |
| |
| # define button for getting prediction |
| if image is not None and st.button("Get prediction"): |
| # load image using PIL |
| input_image = Image.open(image) |
|
|
| # show image |
| st.image(input_image, use_column_width=True) |
| |
| # get prediction |
| confidence, label = predict(input_image) |
| |
| # print results |
| "Model is", confidence, "% confident that this image is of a", label |
| 1.3 Deploy DL model |
| # run streamlit app |
| !streamlit run app.py &>/dev/null& |
| # make streamlit app available publicly |
| from pyngrok import ngrok |
| |
| public_url = ngrok.connect('8501'); |
| |
| public_url |
| Model can be deployed on AWS/Colab/Flask/Hugging Spaces |
| Hugging spaces model |
| https://huggingface.co/spaces/ArunkumarCH/BirdClassification |
