AI & Data
Computer Vision Challenges
Computer Vision challenges put you to work teaching machines to see. You'll develop skills in Image Processing and CNN Classification, build pipelines with OpenCV, tackle Object detection and Segmentation, and adapt pretrained models through Transfer learning.
From there you'll handle the harder edges — Custom architectures, 3D vision, Real-time inference, and Computer Graphics — building and deploying vision systems the way applied research teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
· CNN Classification Clear- AnalysisIntermediateNew
Detect Defects on a Production Line for a Tier-1 Auto Supplier
You receive 12,000 labelled grayscale part images (8,000 good, 4,000 defective across 6 defect types) at 2048x2048. Build a pipeline that does classical preprocessing (illuminat…
- Defect Detection
- Image Classification
- Image Preprocessing
Open coursework - CodeFoundationalNew
Classify Retail Product Photos for an E-Commerce Marketplace
Use a publicly-available product-image dataset (e.g., Fashion-MNIST extended, or a Kaggle e-commerce subset of around 10k images across 12 categories). Fine-tune a small pretrai…
- Image Classification
- Transfer Learning
- Pytorch
Open coursework - CodeBeginnerNew
Semantic Segmentation for a Solar-Panel Inspection Drone
Use a publicly-available solar-panel dataset (or the PV-Defect-Detection dataset). Fine-tune a small U-Net or SegFormer-tiny on panel/no-panel pixel-level segmentation. Evaluate…
- Semantic Segmentation
- Cnn
- Transfer Learning
Open coursework - CodeBeginnerNew
Build a Crop-Disease Classifier for a Smallholder Agritech Startup
You receive a curated 22,000-image cassava-disease dataset across 5 classes (4 diseases + healthy) plus a labeled 1,200-image held-out test set. Train a CNN classifier (start wi…
- Image Classification
- Cnn Architectures
- Transfer Learning
Deep Learning for Computer Vision Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Triage Medical-Imaging Annotations with a Small Vision Model
Train a binary normal/abnormal classifier on the public CheXpert or NIH ChestX-ray14 dataset. Use temperature scaling to calibrate the output, then define abstention thresholds …
- Image Classification
- Transfer Learning
- Calibration
Applied Machine Learning - CodeSeniorNew
Build a Visual Search Prototype for Sustainable Fashion
Using the public fashion image dataset and the supplemental product metadata table provided, build an end-to-end visual search pipeline: extract a numeric fingerprint (an embedd…
- Deep Learning
- Computer Vision
- Cnn
Open coursework
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.