Computer Vision Tasks

Computer vision enables machines to interpret and understand visual information from the world. From identifying objects in images to generating photorealistic scenes, computer vision models power applications ranging from autonomous vehicles to medical diagnostics.

Video Tutorials

Learn how to work with computer vision models through our video guides:

• Train a Computer Vision Model - Complete walkthrough using ViT Large for image classification
• Run Inference on Computer Vision Models - How to use trained models for predictions
• Deploy Computer Vision Models - Production deployment strategies

Image Understanding Tasks

Image Classification

Assign a single label to an entire image. The most fundamental computer vision task.

Examples: Is this image a cat or dog? What breed is this bird? Does this X-ray show disease?

Available models:

• Vision Transformers: ViT Base, ViT Large, ViT Small MSN
• ResNet family: ResNet-18, ResNet-50, ResNet-101
• Efficient models: EfficientNet B0, MobileNet V3 Small

Learn more: Image Classification Concepts

Object Detection

Locate and classify multiple objects within an image using bounding boxes.

Examples: Find all pedestrians in a street scene, detect products on a shelf, identify tumors in medical scans

Available models:

• DETR family: DETR ResNet-50, DETR ResNet-101, DETR ResNet-50 DC5, DETR ResNet-101 DC5
• Improved DETR: DAB-DETR ResNet-50, Conditional DETR, Deformable DETR
• Real-time: YOLOv8 Nano

Learn more: Object Detection Concepts

Image Segmentation

Classify every pixel in an image, creating precise masks for each object or region.

Examples: Segment organs in medical images, separate foreground from background, autonomous driving scene understanding

Available models:

• DETR Segmentation: DETR Segmentation ResNet-50, DETR Segmentation ResNet-101, DETR Segmentation ResNet-50 DC5
• Specialized: Mask R-CNN, SAM (Segment Anything), SegFormer B0

Learn more: Image Segmentation Concepts

Keypoint Detection

Locate specific points of interest on objects, typically used for pose estimation.

Examples: Human pose estimation, facial landmark detection, hand tracking

Available models: ViTPose

Learn more: Keypoint Detection Concepts

Zero-Shot Image Classification

Classify images into categories never seen during training using learned visual representations.

Examples: Classify into new product categories without retraining, recognize rare diseases, identify unusual objects

Available models: Prototypical Network

Learn more: Zero-Shot Classification Concepts

3D Understanding Tasks

Depth Estimation

Predict the distance of every pixel from the camera, creating a 3D understanding from 2D images.

Examples: Autonomous navigation, AR/VR applications, 3D reconstruction, bokeh effects

Available models: Depth Anything

Learn more: Depth Estimation Concepts

Generative Tasks

Text-to-Image

Generate photorealistic images from natural language descriptions.

Examples: Create product images from descriptions, generate art, design concept visualization

Available models: Stable Diffusion v1.5

Learn more: Text-to-Image Generation Concepts

Model Architectures

Computer vision models are built on several core architectures:

Vision Transformers (ViT): Treat images as sequences of patches, using self-attention mechanisms. State-of-the-art accuracy on large datasets but require substantial data.

Convolutional Neural Networks (CNNs): ResNet, EfficientNet, and MobileNet use convolutional layers to detect visual patterns. Faster inference and better on smaller datasets than ViT.

Hybrid Architectures: Models like DETR combine CNN backbones with transformer processing, leveraging strengths of both approaches.

Diffusion Models: Stable Diffusion generates images by iteratively denoising random inputs, guided by text embeddings.

Key Characteristics of Computer Vision

High-dimensional data: Images contain thousands to millions of pixels. A 224×224 RGB image has 150,528 input values.

Spatial structure: Nearby pixels are strongly correlated. Models must learn to recognize patterns across different positions, scales, and orientations.

Transfer learning: Pre-trained models on large datasets (ImageNet, COCO) provide powerful starting points for custom tasks.

Data augmentation: Techniques like rotation, flipping, color jittering, and cropping artificially expand training data and improve generalization.

GPU requirements: Computer vision models are computationally intensive. Training typically requires GPUs with 8GB+ VRAM, inference can run on smaller GPUs or even CPUs.

Choosing the Right Model

For image classification:

• Start with ResNet-50 for balanced speed and accuracy
• Use ViT Large for maximum accuracy with large datasets (10k+ images)
• Choose MobileNet V3 or EfficientNet B0 for edge deployment

For object detection:

• Use YOLOv8 Nano for real-time applications (60+ FPS)
• Choose DETR ResNet-50 for end-to-end simplicity and good accuracy
• Pick Deformable DETR for best accuracy on small objects

For segmentation:

• Use SAM for interactive segmentation and zero-shot capability
• Choose SegFormer B0 for efficient semantic segmentation
• Pick Mask R-CNN for instance segmentation

For specialized tasks:

• ViTPose for human pose estimation
• Depth Anything for monocular depth prediction
• Stable Diffusion for image generation
• Prototypical Network for few-shot/zero-shot classification

Practical Workflow

1. Define the task: Classification, detection, segmentation, or generation?
2. Prepare data: Organize images in folders, create annotations for detection/segmentation
3. Choose model: Consider dataset size, accuracy needs, inference speed requirements
4. Configure training: Set batch size based on GPU memory, adjust learning rate, choose epochs
5. Train: Monitor validation metrics, check for overfitting, use early stopping
6. Evaluate: Test on held-out data, analyze failure cases, check edge cases
7. Deploy: Export to ONNX/TorchScript, optimize for production, set up inference pipeline
8. Monitor: Track prediction quality, retrain with new data as needed

Common Challenges

Small datasets: Use transfer learning with pre-trained models, apply heavy data augmentation, consider few-shot learning approaches.

Class imbalance: Oversample minority classes, use weighted loss functions, collect more balanced data.

Out of memory: Reduce batch size, use gradient accumulation, lower image resolution, use mixed precision training.

Overfitting: Add data augmentation, use regularization (dropout, weight decay), reduce model size, collect more data.

Slow training: Use smaller model variant (ResNet-18 instead of ResNet-101), reduce image resolution, use more GPUs for distributed training.

Poor generalization: Ensure training data matches deployment scenarios, add domain-specific augmentation, use domain adaptation techniques.