You will get an image classifier that can classify multiple categories of images.
Project details
I will deliver a fully functional image classification application, available as either a web or desktop application. The app will classify images into various categories using a TensorFlow neural network model. It features an intuitive user interface built with Flask for web-based access or Tkinter for a desktop experience, making it easy to upload images and receive instant classification results.
Machine Learning Tools
Python, TensorFlowWhat's included
| Service Tiers |
Starter
$15
|
Standard
$30
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 3 | 9 | Unlimited |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 2 | 5 | 7 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
About Al-Amin
Python Developer
Abuja, Nigeria - 2:19 am local time
Skills:
C++: Experienced in developing console and GUI applications.
Python: Proficient in building machine learning models using libraries like TensorFlow, Scikit-learn, and Pandas.
Web Development: Skilled in creating user-friendly web apps using Flask.
My Recent Projects:
Developed a book recommendation system utilizing a nearest neighbors algorithm for personalized suggestions.
Built an animal classification app using a TensorFlow-based neural network and Flask for a seamless user interface
Built an offensive statement detector using a simple neural network architecture
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Regular communication and collaboration are essential to me, so let's keep in touch to bring your ideas to life!
Steps for completing your project
After purchasing the project, send requirements so Al-Amin can start the project.
Delivery time starts when Al-Amin receives requirements from you.
Al-Amin works on your project following the steps below.
Revisions may occur after the delivery date.
Gather Data
If the client does not provide the dataset, I will source appropriate data based on the specified categories.
Data Cleaning and Preprocessing
Clean and preprocess the data to ensure it is in a suitable format for training, including normalization and augmentation if necessary.