Computer-Vision-Internship

An AI based computer vision internship

🧠 Computer Vision Internship using Roboflow

A Not-for-Profit, Open-Source Learning Experience
Created by Daniel Christadoss

Learn Computer Vision from fundamentals to real-world warehouse digital twin creation.
Build your own dataset, train models in Roboflow, and deploy intelligent inventory systems.

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🎯 Program Overview

This internship program introduces hobbyists, students, and innovators to Computer Vision and AI-based automation.
Participants will begin by detecting simple objects and progress toward building a warehouse digital twin capable of visual inventory management.

The program is self-paced, hands-on, and community-driven — with guidance through posts, GitHub modules, and collaborative examples.

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🧩 Learning Objectives

By the end of this program, participants will:

• Understand dataset creation, annotation, and model training using Roboflow.
• Run real-time object detection and inference on local devices or in the cloud.
• Manage datasets, models, and documentation using GitHub.
• Integrate computer vision outputs with inventory management systems.
• Develop a capstone project: a functional warehouse digital twin.

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🗂️ 10-Module Curriculum

Module 1 — Orientation & Setup

Objectives:

• Learn about computer vision, machine learning, and Roboflow’s ecosystem.
• Set up your Roboflow and GitHub accounts.
• Review introductory videos and resources.

Deliverables:

• Screenshot of Roboflow dashboard setup
• GitHub repo initialized for internship work

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Module 2 — Fundamentals of Image Datasets

Objectives:

• Understand dataset structure: train / validation / test.
• Explore dataset formats (COCO, YOLO, Pascal VOC).
• Upload your first dataset to Roboflow.

Deliverables:

• A small dataset uploaded and versioned in Roboflow

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Module 3 — Annotation & Labeling Best Practices

Objectives:

• Learn to annotate images accurately using Roboflow tools.
• Understand class naming, consistency, and data hygiene.
• Apply version control to datasets.

Deliverables:

• Annotated dataset with at least 2 object classes

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Module 4 — Model Training & Evaluation

Objectives:

• Select model type (object detection, classification, segmentation).
• Train a base model and interpret metrics (Precision, Recall, mAP).
• Review training curves and validation images.

Deliverables:

• Screenshot of trained model metrics page

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Module 5 — Running Object Detection Inference

Objectives:

• Perform inference on static images, videos, and live webcam feeds.
• Explore Roboflow Inference API and visualization outputs.
• Debug mis-detections and improve model accuracy.

Deliverables:

• Sample inference results and JSON output

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Module 6 — GitHub Workflow & Collaboration

Objectives:

• Learn Git basics: clone, commit, push, pull request.
• Maintain your project repository with datasets, notebooks, and code.
• Document your journey via markdown READMEs and issue logs.

Deliverables:

• Functional GitHub repo with project documentation

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Module 7 — Integration & Deployment

Objectives:

• Export trained model (TensorFlow, PyTorch, Edge Impulse, or API).
• Deploy and test inference on edge devices (Raspberry Pi / XIAO MG24 / ESP32).
• Connect model outputs to Node-RED or MQTT pipelines.

Deliverables:

• Screenshot or short demo of local deployment

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Module 8 — Applied Computer Vision Scenarios

Objectives:

• Implement object counting and tracking.
• Identify misplaced or missing objects using bounding box logic.
• Explore occupancy, area, and movement analysis.

Deliverables:

• Simple use-case demonstration (e.g., item counter or misplaced object alert)

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Module 9 — From Object Detection to Inventory Management

Objectives:

• Link detection results to an inventory database (e.g., TimescaleDB, SQLite).
• Automate updates using Node-RED or Python scripts.
• Create simple dashboards in Grafana or web UI.

Deliverables:

• Dashboard showing live or simulated inventory data

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Module 10 — Capstone: Warehouse Digital Twin

Objectives:

• Map a small warehouse/lab with a camera or drone feed.
• Train a Carton Library (SKU / package recognition).
• Build a live digital twin dashboard integrating camera and database.
• Document and publish your project to GitHub.

Deliverables:

• Final report + demo video + GitHub repository
• Optional LinkedIn showcase post

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💡 Tools & Technologies

• Roboflow — dataset, training, and inference platform
• GitHub — collaboration and documentation
• Python / Node-RED / MQTT — data integration
• Grafana / TimescaleDB — visualization and analytics
• Edge Devices — Raspberry Pi, Seeed XIAO MG24, ESP32

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🤝 Community & Mentorship

This is a not-for-profit, open-source learning initiative.
The internship is designed to encourage curiosity, independent research, and creative experimentation.

“It is not a spoon-fed curriculum. It expects you to do the legwork.”
— Daniel Christadoss

Collaborators, mentors, and hobbyists are welcome to contribute.

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🧭 Next Steps

1. Create your Roboflow and GitHub accounts.
2. Follow the learning posts and commit weekly progress.
3. Share updates and tag your mentor or peers on LinkedIn.
4. Prepare your final Capstone Submission.

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License: MIT (for learning & community sharing)
Contact: LinkedIn – Daniel Christadoss
Program Type: Educational / Not-for-Profit / Open-Source

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