Phoenix - Your AI AssistantBuild a computer vision model using deep learning
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4 days
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What you get with this Offer
Turn your images or videos into a reliable Computer Vision proof of concept built for a real business objective.
I will design, train and evaluate one Deep Learning solution using Python, PyTorch and OpenCV. The model and evaluation strategy will be selected for your task—not copied from a generic template.
SUITABLE USE CASES
Image classification, object detection, segmentation, visual inspection, defect detection, OCR and recorded-video analysis.
TECHNICAL APPROACH
✓ Define the task, target classes and success criteria
✓ Audit image quality, labels, class balance and corrupted files
✓ Detect duplicate or near-duplicate images
✓ Create train, validation and test splits without data leakage
✓ Select a suitable pretrained CNN, Vision Transformer or YOLO
✓ Apply task-appropriate resizing and augmentation
✓ Handle imbalance using weighting, sampling or focal loss when relevant
✓ Fine-tune with early stopping and learning-rate scheduling
✓ Evaluate performance on an unseen test set
✓ Analyse per-class errors, false positives and false negatives
✓ Measure inference time and model size
✓ Provide Grad-CAM explanations when applicable
METRICS ADAPTED TO YOUR TASK
Classification may use precision, recall, F1, ROC-AUC and a confusion matrix. Detection may use mAP, IoU and per-class AP. Segmentation may use Dice and IoU, while OCR may use character or word error rate.
The model will be selected using technical results and operational needs such as missed detections, false alarms, latency and deployment constraints.
YOU WILL RECEIVE
• One trained and evaluated Computer Vision model
• Model weights and reproducible preprocessing pipeline
• Clean, documented Python code
• Training and evaluation notebook or scripts
• Test-set metrics and error analysis
• Example predictions and visual outputs
• Technical report and README
• One revision and seven days of support
BASE OFFER SCOPE
• One clearly defined Computer Vision task
• One labelled image dataset
• Up to 5,000 images
• Images or recorded video
• One pretrained model family and final model
• One proof-of-concept workflow
For data labelling, real-time tracking, multiple cameras, edge optimisation, API deployment, dashboards or larger datasets, please message me for a tailored scope.
Please provide your objective, sample images or videos, class definitions, labels, expected output and operational constraints.
I will design, train and evaluate one Deep Learning solution using Python, PyTorch and OpenCV. The model and evaluation strategy will be selected for your task—not copied from a generic template.
SUITABLE USE CASES
Image classification, object detection, segmentation, visual inspection, defect detection, OCR and recorded-video analysis.
TECHNICAL APPROACH
✓ Define the task, target classes and success criteria
✓ Audit image quality, labels, class balance and corrupted files
✓ Detect duplicate or near-duplicate images
✓ Create train, validation and test splits without data leakage
✓ Select a suitable pretrained CNN, Vision Transformer or YOLO
✓ Apply task-appropriate resizing and augmentation
✓ Handle imbalance using weighting, sampling or focal loss when relevant
✓ Fine-tune with early stopping and learning-rate scheduling
✓ Evaluate performance on an unseen test set
✓ Analyse per-class errors, false positives and false negatives
✓ Measure inference time and model size
✓ Provide Grad-CAM explanations when applicable
METRICS ADAPTED TO YOUR TASK
Classification may use precision, recall, F1, ROC-AUC and a confusion matrix. Detection may use mAP, IoU and per-class AP. Segmentation may use Dice and IoU, while OCR may use character or word error rate.
The model will be selected using technical results and operational needs such as missed detections, false alarms, latency and deployment constraints.
YOU WILL RECEIVE
• One trained and evaluated Computer Vision model
• Model weights and reproducible preprocessing pipeline
• Clean, documented Python code
• Training and evaluation notebook or scripts
• Test-set metrics and error analysis
• Example predictions and visual outputs
• Technical report and README
• One revision and seven days of support
BASE OFFER SCOPE
• One clearly defined Computer Vision task
• One labelled image dataset
• Up to 5,000 images
• Images or recorded video
• One pretrained model family and final model
• One proof-of-concept workflow
For data labelling, real-time tracking, multiple cameras, edge optimisation, API deployment, dashboards or larger datasets, please message me for a tailored scope.
Please provide your objective, sample images or videos, class definitions, labels, expected output and operational constraints.
What the Freelancer needs to start the work
Please provide:
• A clear description of the business problem
• Sample images or videos
• The complete labelled dataset, if available
• Class names and annotation format
• Examples of the expected output
• The business cost of missed detections and false alarms
• Intended hardware and latency requirements
• Any privacy, licensing or deployment constraints
Please message me before ordering so I can verify the dataset quality, labelling and technical feasibility.