Image understanding
Train or adapt networks for visual classification and feature extraction.
Deep Learning Development
Neural-network development for image, sequence and text tasks, from dataset preparation and fine-tuning to inference integration.
InputHidden layersOutput
CheckpointHeld-out scansInference
Tools & technologies
- PyTorch or TensorFlow
- GPU training
- Experiment tracking
- Model serving
When patterns outgrow hand-written rules.
Sequence modelling
Learn patterns in time-series or event histories with time-aware evaluation.
Model adaptation
Fine-tune a suitable pretrained model for a narrowly defined task.
Training work that can be reproduced.
- Dataset preparation code
- Training configuration and checkpoints
- Baseline comparison report
- Inference packaging and resource profile
- Dataset preparation code
- Training configuration and checkpoints
- Baseline comparison report
- Inference packaging and resource profile
Classify a document image
Capability Example
Scanned forms arrive with mixed layouts and imperfect image quality.
Illustrative data. Nothing is sent to an external system.
Example workspace1 / 3
Prepared scan
- Source: scanner B
- Layout: application form
- Evaluation split: held out
Label document types and separate evaluation scans by source.
Adapted network
- Checkpoint: document-v1
- Compared with baseline
- Unfamiliar layout detected
Fine-tune a pretrained network and compare it with the baseline.
Review record
- Suggested type: unknown
- Action: manual sorting
- Failure example retained
Route low-confidence or unfamiliar layouts to manual sorting.
A document-type score and review status, with inference time and failure cases recorded.
Follow the record through the next step
Size the network around the constraint.
Compute budget
Set training and inference resource limits before selecting the network.
Data coverage
Test rare layouts and real capture conditions, not only clean training images.
Questions before we start.
Must we train from scratch?
Usually the first comparison includes pretrained models and simpler baselines. Training from scratch needs a specific justification.
Can a model run on a device?
Potentially. Model size, supported operators, latency and device memory need measurement on the target hardware.
Bring your starting point.
Bring representative inputs, the target output and the hardware or latency constraints.
