Computer Vision Internship
Teach computers to see: image processing with OpenCV, image classification with transfer learning, object detection with YOLO, tracking, OCR, segmentation and deploying vision models as apps.
What you'll work on
- Level 1: Foundations - Images as numbers, Basic image operations, Drawing and text and more
- Level 2: Building real features - Transfer learning, Data augmentation, Label images for detection and more
- Level 3: Professional work and capstone - Deploy a vision API, Web app for your model, Optimise for edge devices and more
Full syllabus: all 30 graded tasks
These are the actual tasks the internship portal assigns for this domain. You submit each one with proof (a GitHub link or document) and a mentor reviews it.
Level 1: Foundations (12 tasks)
- Images as numbers. In a repo named cv-internship, load an image with OpenCV and explain pixels, channels (BGR vs RGB), shape and data type with printed examples.
- Basic image operations. Resize, crop, rotate, flip and convert an image to grayscale. Save each result and show them in one grid in a notebook.
- Drawing and text. Draw rectangles, circles and text labels on an image, as an object detector would, and save the annotated image.
- Histograms and contrast. Plot the histogram of a dark photo, then improve it with histogram equalization and CLAHE. Compare the results.
- Filters and blur. Apply average, Gaussian, median and bilateral filters to a noisy image and explain which one removes salt-and-pepper noise best.
- Edge detection. Use Sobel and Canny edge detection on 3 images and tune the Canny thresholds. Explain what the thresholds do.
- Thresholding and contours. Count coins or objects in a photo using thresholding and contour detection, and draw a box around each one.
- Webcam capture. Open your webcam with OpenCV, show the live frame rate on screen and save a frame when a key is pressed.
- Colour detection. Track an object of one colour in a webcam video using HSV colour ranges and draw its path.
- Face detection. Detect faces in photos and the webcam with OpenCV's Haar cascade or MediaPipe and count the faces found.
- Build an image dataset. Collect or download 200 or more images for 3 classes from a free dataset (Kaggle or Roboflow Universe), split them into train, validation and test folders and note the licence.
- Image classifier with a pretrained model. Classify 20 of your own photos with a pretrained ResNet or MobileNet in PyTorch or Keras and show the top-3 predictions for each.
Level 2: Building real features (10 tasks)
- Transfer learning. Fine-tune MobileNet or EfficientNet on your 3-class dataset. Report accuracy, a confusion matrix and 5 images it got wrong.
- Data augmentation. Train the same model with and without augmentation (flip, rotate, colour jitter) and compare the validation accuracy curves.
- Label images for detection. Label 150 or more images with bounding boxes in Label Studio, CVAT or Roboflow and export them in YOLO format.
- Train an object detector. Train a YOLO model (Ultralytics) on your labelled data. Report mAP50, precision and recall and show detections on new images.
- Real-time detection. Run your detector on webcam or video and show the frames per second. Try a smaller model size and compare speed and accuracy.
- Object tracking and counting. Track people or vehicles across video frames (ByteTrack or DeepSORT) and count how many cross a line.
- OCR. Read text from 10 photos of bills or name boards with Tesseract or EasyOCR. Pre-process the images and report how accuracy changed.
- Image segmentation. Run a segmentation model (YOLO-seg, SAM or U-Net) to separate objects from the background and save the masks.
- Pose estimation. Use MediaPipe Pose or YOLO-pose to count squats or push-ups from a video using joint angles.
- Image search with embeddings. Use CLIP to embed 200 images and search them by text, like 'red car' or 'a classroom', and show the top results.
Level 3: Professional work and capstone (8 tasks)
- Deploy a vision API. Serve your detector as a FastAPI endpoint that takes an image and returns boxes as JSON. Add file size and type checks.
- Web app for your model. Build a Streamlit or Gradio app where users upload a photo and see predictions, and host it free on Hugging Face Spaces.
- Optimise for edge devices. Export your model to ONNX or TensorFlow Lite, quantize it, and compare size and speed with the original.
- Error analysis. Find 20 failure cases of your model, group them by cause (lighting, size, blur, angle) and fix the biggest group with more data or augmentation.
- Privacy and bias check. Write how your app handles faces and personal images (consent, storage, blurring) and test whether accuracy differs across skin tones or lighting.
- Industrial use case. Build a defect-detection demo for a real local need (bottle caps, PCB solder, fabric flaws or fruit quality) with a dataset, model and results.
- Document the model. Write a model card: data, training settings, metrics, known limits and intended use.
- Capstone: vision product. Ship a complete computer vision app (attendance by face with consent, parking counter, crop disease checker or similar) with a live demo link, repo and a 2-minute video.
Real software work, not just training
Every Computer Vision task is reviewed by our team and saved to your portfolio. Interns who pass the readiness check can take real work from Training Trains, W3AppDevelopers and DomainHostly projects on the Company Work board.
Frequently asked questions
Do I need experience to join the Computer Vision internship?+
Basic Python helps. A laptop webcam is enough; model training runs on free Colab or Kaggle GPUs.
What will I do in the Computer Vision internship?+
You complete 30 graded tasks in three levels: foundations, real features, and professional work. Each task is submitted with GitHub or document proof and reviewed. The final capstone is: Capstone: vision product.
Do I get a certificate for the Computer Vision internship?+
Yes. After your required tasks are approved, your attendance is above 75% and you pass the proctored final exam, you get a graded completion certificate that anyone can verify at trainingtrains.com/verify.
Can I do the Computer Vision internship online?+
Yes. You can do it fully online from home, or offline at our Erode and Perundurai branches. Tasks, quizzes and mentor reviews are the same in both modes.
Available both online (work from home) and offline at our Erode and Perundurai branches - see the full internship program details for departments, duration, and the application process.