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I will build a CNN image classification python model using tensorflow
I turn raw data into powerful dashboards and insights
I'm Anfal Iftikhar,a BS Artificial Intelligence student
specializing in Data Analysis, Power BI dashboards,and
Python-based data solutions.I've worked as a Data Analyst
Intern at Analytix Camp and a P...
About this Gig
Do you need a reliable developer to build a CNN image classification Python model for your project? I build custom Convolutional Neural Networks that accurately identify and sort images into categories using deep learning.
I will deliver a high-accuracy CNN image classification Python model using TensorFlow and Keras including transfer learning with VGG16, ResNet, or MobileNet for maximum performance even on small datasets.
What my CNN image classification Python service includes:
Custom CNN architecture or transfer learning model for your images
Full data pipeline:loading, augmentation, normalization
Training with callbacks:early stopping and model checkpoint
Evaluation: accuracy, loss curves, confusion matrix, classification report
Clean Python code+Jupyter notebook
Saved model (.h5/.keras) with prediction script
Use cases for this CNN image classification Python model:
Plant disease and crop classification
Medical image classification
Custom object or animal recognition
Ready to get your CNN image classification Python model built by an experienced deep learning. Message me with your image dataset details before ordering and I will recommend the best architecture.
APIs:
Microsoft Computer Vision AI
Programming language:
Python
•
SQL
•
Colab
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
Colab
Frameworks:
Scikit-learn
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Keras
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PyTorch
•
Panda
My Portfolio
FAQ
What image format and size do you need?
JPG, PNG, or BMP images work fine. No minimum resolution required.I resize and normalize images as part of the preprocessing pipeline. Organize your images into folders by class before sharing.
How many images do I need per class?
For a custom CNN, at least 200–500 images per class is ideal. For transfer learning, even 50–100 images per class can produce strong results thanks to pre-trained weights.
I have no labels.can you still classify my images?
If your images are unlabeled, I can use unsupervised clustering or a pre-trained zero-shot model to group similar images. Message me first with your dataset and goal.
