Welcome to resnet50-Deep-Learning-image-classifier 👋

ResNet-50 is a CNN(convolutional neural network) that is 50 layers deep. we can load a pretrained version of the network trained on more than a million images from the ImageNet database. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, birds and many animals.

🏠 Homepage

✨ Demo

Deployment

Everything you need to get started. Google Cloud. Start building right away on our secure, intelligent platform. New customers can use a $300 free credit to get started with any Google Cloud. https://console.cloud.google.com/

genral note (for Qwiklabs users) :- If you use resources that are not related to the lab, you could be locked out of the lab and lose your work. For example, if you start up more VMs than the number called for in the lab instructions, your lab will end, you will be locked out of Qwiklabs, and you will receive the message "Your account has been blocked".torial may block your Qwiklabs account.It is always good to have one personal GCP account. New customers can use a $300 free credit to get started with Google Cloud.

Step 0:

Activate Cloud Shell

In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button. after that Click Continue.

step 1:

Clone this repo

clone this repo via following command

git clone https://github.com/viveks-codes/resnet50-Deep-Learning-image-classifier.git

Change directory into resnet50-Deep-Learning-image-classifier

cd resnet50-Deep-Learning-image-classifier

step 2:

Authenticate API Requests

Set an environment variable for [YOUR_PROJECT_ID], replacing [YOUR_PROJECT_ID] with your own project ID:

export PROJECT_ID=[YOUR_PROJECT_ID]

if you are using qwiklabs account run following (for qwiklabs)

gcloud iam service-accounts create qwiklab \
  --display-name "My Qwiklab Service Account"

Create a Service Account to access the Google Cloud APIs when testing locally:

gcloud iam service-accounts create resnet50 \
  --display-name "My GCP Account"

if you are using qwiklabs account run following (for qwiklabs)

gcloud iam service-accounts create qwiklab \
  --display-name "My Qwiklab Service Account"

Give your newly created Service Account appropriate permissions:

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
--member serviceAccount:resnet50@${PROJECT_ID}.iam.gserviceaccount.com \
--role roles/owner

if you are using qwiklabs account run following (for qwiklabs)

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
--member serviceAccount:qwiklab@${PROJECT_ID}.iam.gserviceaccount.com \
--role roles/owner

After creating your Service Account, create a Service Account key:

gcloud iam service-accounts keys create ~/key.json \
--iam-account resnet50@${PROJECT_ID}.iam.gserviceaccount.com

if you are using qwiklabs account run following (for qwiklabs)

gcloud iam service-accounts keys create ~/key.json \
--iam-account qwiklab@${PROJECT_ID}.iam.gserviceaccount.com

export GOOGLE_APPLICATION_CREDENTIALS="/home/${USER}/key.json"

Run tests

Create environment

virtualenv -p python3 env

activate the environment

source env/bin/activate

step 3:

Install dependencies

pip install -r requirements.txt

Creating an App Engine App

create an App Engine instance by using:

gcloud app create

Create a Storage BucketCreating a Storage Bucket

export CLOUD_STORAGE_BUCKET=${PROJECT_ID}

create a bucket

gsutil mb gs://${PROJECT_ID}

Running the Application

Execute the following command to start your application:

python main.py

Once the application starts, click on the Web Preview icon in the Cloud Shell toolbar and choose "Preview on port 8080."

Deploying the App to App Engine

Update your Cloud Build timeout:

gcloud config set app/cloud_build_timeout 1000

Deploy your app on App Engine by using gcloud

gcloud app deploy

If asked, Do you want to continue (Y/n), press Y and then Enter.

After the application is deployed, open the app in your web browser with the following URL:

https://<PROJECT_ID>.appspot.com

Now You are all set!⭐️

Author

👤 vivek patel

• Website: http://viveks.codes/vivek-codes
• Github: @viveks-codes
• LinkedIn: @vivek-patel-0553731a5

🤝 Contributing

Contributions, issues and feature requests are welcome!
Feel free to check issues page. You can also take a look at the contributing guide.

resources

Deploying a Python Flask Web Application to App Engine Flexible

Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Show your support

Give a ⭐️ if this project helped you!

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