Computer Vision Helper

Computer Vision Helper

The Computer Vision Helper skill guides you through implementing image analysis and visual AI tasks. From basic image classification to complex object detection and segmentation, this skill helps you leverage modern computer vision techniques effectively.

Computer vision has been transformed by deep learning and now by vision-language models. This skill covers both traditional approaches (CNNs, pre-trained models) and cutting-edge techniques (CLIP, GPT-4V, Segment Anything). It helps you choose the right approach based on your accuracy requirements, available data, and deployment constraints.

Whether you are building product recognition, document analysis, medical imaging, or any visual AI application, this skill ensures you understand the landscape and implement solutions that work.

Core Workflows

Workflow 1: Select Computer Vision Approach

1. Define the task:
   • Classification: What category is this image?
   • Detection: Where are objects in this image?
   • Segmentation: Pixel-level object boundaries
   • OCR: Extract text from images
   • Similarity: Find similar images
   • Generation: Create or modify images
2. Assess available resources:
   • Training data quantity and quality
   • Compute budget (training and inference)
   • Latency requirements
   • Accuracy needs
3. Choose approach:

   Task	No Training Data	Small Dataset	Large Dataset
   Classification	CLIP, GPT-4V	Transfer learning	Fine-tune/train
   Detection	GPT-4V, Grounding DINO	Fine-tune YOLO	Train custom
   Segmentation	SAM	Fine-tune SAM	Train custom
   OCR	Cloud APIs, Tesseract	Fine-tune	Train custom

4. Plan implementation
5. Document approach rationale

Workflow 2: Implement Image Classification

1. Prepare data:

   # Data loading with augmentation
   transform = transforms.Compose([
       transforms.Resize(256),
       transforms.CenterCrop(224),
       transforms.RandomHorizontalFlip(),
       transforms.ColorJitter(brightness=0.2, contrast=0.2),
       transforms.ToTensor(),
       transforms.Normalize(mean=[0.485, 0.456, 0.406],
                          std=[0.229, 0.224, 0.225])
   ])
   
   dataset = ImageFolder(root='data/', transform=transform)
   dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
   

2. Set up model:

   # Transfer learning from pretrained model
   model = models.resnet50(pretrained=True)
   
   # Freeze early layers
   for param in model.parameters():
       param.requires_grad = False
   
   # Replace classifier head
   model.fc = nn.Linear(model.fc.in_features, num_classes)
   

3. Train with validation
4. Evaluate on test set
5. Optimize for deployment