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I will train custom CNN image classification models in pytorch or tensorflow
Computer Vision Developer YOLO, OpenCV, PyTorch, TensorFlow
I am a Computer Vision and AI Engineer specializing in Machine Learning, Deep Learning, OpenCV, YOLO, PyTorch, and TensorFlow. I develop intelligent solutions for object detection, image classificatio...
About this Gig
Need an image classification model that works on new, unseen images? I build, train and tune CNN and transfer-learning classifiers in PyTorch or TensorFlow on your own dataset, and show proof: accuracy, F1 and a confusion matrix.
WHAT I BUILD
- Binary, multi-class and multi-label image classification
- Transfer learning (ResNet, EfficientNet, MobileNet) or custom CNNs
- Fixes for overfitting, class imbalance and low accuracy
- Grad-CAM heatmaps that show what the model looks at
- Deployment: Gradio demo, FastAPI, ONNX / TFLite export
WHAT YOU GET
- Trained model file + clean, commented Python code
- Report: accuracy, precision, recall, F1, confusion matrix
- Inference script to classify new images in one command
HOW IT WORKS: you send your images and goal, I clean and split the data, train, tune and validate, then deliver results and support.
TECH STACK: Python, PyTorch, TensorFlow, Keras, scikit-learn, torchvision, Google Colab
Need object detection or tracking instead? See my YOLO gig. Message me before ordering with your classes and image count, and I will recommend the right package.
FAQ
Can you train a custom image classification model on my images?
Yes. Send your labeled images (one folder per class, or a CSV of labels). I train a CNN or transfer-learning model in PyTorch or TensorFlow, validate it, and deliver the model, code and an accuracy report.
How many images do I need for image classification?
It depends on the task. With transfer learning and augmentation, a few hundred images per class often works, and more data usually helps. Send me a sample and I will tell you honestly what to expect.
Should I use a custom CNN or transfer learning?
For most small and medium datasets, transfer learning (ResNet, EfficientNet, MobileNet) is faster and more accurate. I pick the approach after a quick test on your data, and compare options in the Standard package.
My model overfits or has low accuracy. Can you fix it?
Yes. I check data quality, class imbalance and data leakage, then apply augmentation, regularization, stronger backbones and tuning. Share your code and dataset and I will show before-and-after metrics.
What is Grad-CAM and will I get it?
Grad-CAM heatmaps show which image regions drive each prediction, which helps you trust and debug the model. They are included in the Standard and Premium packages.
Can the model run on mobile, the web or through an API?
Yes. Premium includes a Gradio demo app, a FastAPI endpoint and ONNX or TFLite export for mobile and edge devices. Other packages can add these as extras.
Which frameworks and tools do you use?
Python with PyTorch (torchvision), TensorFlow/Keras, scikit-learn and Google Colab. Tell me your preferred framework and I will match it.
Can I use the trained model in a commercial product?
You receive the full code and trained model for your project. Some pretrained weights have their own licenses, so tell me if your use is commercial and I will choose suitable backbones.
Do you provide model evaluation and performance analysis?
Yes. Depending on the package, I can provide training/validation results, performance metrics, evaluation analysis, and testing/inference results.
