Who should use the Classify images workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for classify images with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A live image classification service that accurately labels new images and provides confidence scores.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A live image classification service that accurately labels new images and provides confidence scores.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Keymakr to a clean, labeled dataset ready for model training, with clear class definitions and a held-out test set for evaluation. Then, you pass the output to imgaug to a standardized, augmented image pipeline that feeds batches into the model, improving generalization. Then, you pass the output to Keras to a trained classification model that achieves high accuracy on the validation set, ready for evaluation. Then, you pass the output to scikit-learn to a quantitative and qualitative understanding of model strengths and weaknesses, with final metrics to report. Then, you pass the output to ONNX (Open Neural Network Exchange) to a production-ready, optimized model that runs efficiently on target hardware with minimal accuracy loss. Finally, MLServer is used to a live image classification service that accurately labels new images and provides confidence scores.
Define classification categories and collect labeled dataset
A clean, labeled dataset ready for model training, with clear class definitions and a held-out test set for evaluation.
Preprocess images and augment data
A standardized, augmented image pipeline that feeds batches into the model, improving generalization.
Select and train a classification model
A trained classification model that achieves high accuracy on the validation set, ready for evaluation.
Evaluate model performance on test set
A quantitative and qualitative understanding of model strengths and weaknesses, with final metrics to report.
Optimize model for deployment (optional)
A production-ready, optimized model that runs efficiently on target hardware with minimal accuracy loss.
Deploy and classify new images
A live image classification service that accurately labels new images and provides confidence scores.
Identify the specific classes (e.g., cat, dog, car) you want the model to recognize. Gather a representative set of labeled images for each class, ensuring balanced distribution and sufficient quantity (at least 100 per class for simple tasks). Split the dataset into training, validation, and test sets (e.g., 70/15/15).
Why Keymakr: Keymakr provides image annotation and labeling capabilities needed to create a labeled dataset for classification categories.
Resize all images to a consistent input size (e.g., 224x224 pixels) and normalize pixel values (e.g., scale to [0,1] or standardize). Apply data augmentation techniques (rotation, flipping, zoom, brightness changes) to the training set to improve model robustness and reduce overfitting.
Why imgaug: imgaug is a library specifically designed for stochastic image augmentation, directly supporting preprocessing and data augmentation needs.
Choose a pre-trained convolutional neural network (e.g., ResNet-50, EfficientNet) for transfer learning, replacing the final classification layer to match your number of classes. Train the model on the training set using a suitable optimizer (e.g., Adam) and loss function (cross-entropy), monitoring validation accuracy to prevent overfitting.
Why Keras: Keras is a high-level deep learning framework suitable for training image classification models with pre-trained model support.
Run the trained model on the held-out test set to compute final metrics: accuracy, precision, recall, F1-score, and confusion matrix. Analyze misclassifications to identify class imbalances or ambiguous categories.
Why scikit-learn: scikit-learn provides evaluation metrics (e.g., accuracy, precision, recall) and tools for classification model performance assessment.
If deploying to production, convert the model to a lightweight format (e.g., ONNX, TensorFlow Lite) and apply quantization or pruning to reduce size and latency. Validate that optimized model maintains acceptable accuracy on the test set.
Why ONNX (Open Neural Network Exchange): ONNX is a standard for model conversion and inference acceleration, ideal for optimizing models for deployment.
Integrate the final model into an application (e.g., REST API, mobile app, batch script) that accepts input images and returns predicted class labels with confidence scores. Set up logging and monitoring to track inference performance and drift over time.
Why MLServer: MLServer supports multi-model serving and cross-framework inference, suitable for deploying classification models to classify new images.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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