Create README.md

README.md

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+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