Computer Vision Guide

Computer Vision Guide

A skill for conducting computer vision research, covering model architectures, dataset preparation, training pipelines, evaluation metrics, and common experimental protocols for image classification, object detection, and segmentation tasks.

Core Tasks and Architectures

Computer Vision Task Taxonomy

Image Classification:
  Input: Single image
  Output: Class label(s)
  Models: ResNet, EfficientNet, ViT, ConvNeXt

Object Detection:
  Input: Single image
  Output: Bounding boxes + class labels
  Models: YOLO (v5-v9), Faster R-CNN, DETR, RT-DETR

Semantic Segmentation:
  Input: Single image
  Output: Per-pixel class label
  Models: U-Net, DeepLab, SegFormer, Mask2Former

Instance Segmentation:
  Input: Single image
  Output: Per-pixel labels distinguishing individual objects
  Models: Mask R-CNN, Mask2Former, SAM

Image Generation:
  Input: Text prompt or noise
  Output: Generated image
  Models: Stable Diffusion, DALL-E, Imagen

Model Architecture Evolution

CNNs (Convolutional Neural Networks):
  LeNet (1998) -> AlexNet (2012) -> VGG (2014) -> ResNet (2015)
  -> EfficientNet (2019) -> ConvNeXt (2022)

Vision Transformers:
  ViT (2020) -> DeiT (2021) -> Swin Transformer (2021)
  -> BEiT (2021) -> DINOv2 (2023)

Trend: Transformers are competitive with CNNs at scale.
Hybrid architectures combining convolutions and attention are common.

Dataset Preparation

Building a Research Dataset

import os
from pathlib import Path


def organize_image_dataset(source_dir: str,
                            split_ratios: dict = None) -> dict:
    """
    Organize images into train/val/test splits.

    Args:
        source_dir: Directory containing class subdirectories
        split_ratios: Dict with 'train', 'val', 'test' ratios
    """
    if split_ratios is None:
        split_ratios = {"train": 0.7, "val": 0.15, "test": 0.15}

    import random
    random.seed(42)

    stats = {}
    for class_dir in sorted(Path(source_dir).iterdir()):
        if not class_dir.is_dir():
            continue

        images = list(class_dir.glob("*.jpg")) + list(class_dir.glob("*.png"))
        random.shuffle(images)

        n = len(images)
        n_train = int(n * split_ratios["train"])
        n_val = int(n * split_ratios["val"])

        stats[class_dir.name] = {
            "total": n,
            "train": n_train,
            "val": n_val,
            "test": n - n_train - n_val
        }

    return stats

Data Augmentation

from torchvision import transforms


def get_training_transforms(img_size: int = 224) -> transforms.Compose:
    """
    Standard data augmentation pipeline for training.

    Args:
        img_size: Target image size
    """
    return transforms.Compose([
        transforms.RandomResizedCrop(img_size, scale=(0.8, 1.0)),
        transforms.RandomHorizontalFlip(p=0.5),
        transforms.ColorJitter(brightness=0.2, contrast=0.2,
                               saturation=0.2, hue=0.1),
        transforms.RandomRotation(15),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225]
        )
    ])

Training Pipeline

Transfer Learning Workflow

import torch
import torch.nn as nn
from torchvision import models


def create_classifier(num_classes: int,
                      backbone: str = "resnet50",
                      pretrained: bool = True) -> nn.Module:
    """
    Create an image classifier using transfer learning.

    Args:
        num_classes: Number of target classes
        backbone: Model architecture name
        pretrained: Whether to use ImageNet-pretrained weights
    """
    if backbone == "resnet50":
        weights = models.ResNet50_Weights.DEFAULT if pretrained else None
        model = models.resnet50(weights=weights)
        model.fc = nn.Linear(model.fc.in_features, num_classes)
    elif backbone == "vit_b_16":
        weights = models.ViT_B_16_Weights.DEFAULT if pretrained else None
        model = models.vit_b_16(weights=weights)
        model.heads.head = nn.Linear(
            model.heads.head.in_features, num_classes
        )
    else:
        raise ValueError(f"Unknown backbone: {backbone}")

    return model

Evaluation Metrics

Metrics by Task

Classification:
  - Top-1 Accuracy: Fraction of correct predictions
  - Top-5 Accuracy: Correct class in top 5 predictions
  - Precision, Recall, F1: Per-class and macro-averaged
  - Confusion Matrix: Visualize class-level errors

Object Detection:
  - mAP (mean Average Precision): Standard COCO metric
  - mAP@0.5: AP at IoU threshold 0.5
  - mAP@0.5:0.95: AP averaged over IoU thresholds 0.5 to 0.95
  - AP per class: Identifies weak categories

Segmentation:
  - mIoU (mean Intersection over Union): Standard metric
  - Pixel Accuracy: Fraction of correctly classified pixels
  - Dice Coefficient: F1 score at the pixel level

Reproducibility Checklist

What to Report in Papers

1. Architecture: Exact model name, number of parameters
2. Pretraining: Dataset and weights used for initialization
3. Training: Optimizer, learning rate schedule, batch size, epochs
4. Augmentation: Full list of augmentations with parameters
5. Hardware: GPU type, number, training time
6. Evaluation: Exact metrics, test set version, evaluation protocol
7. Code: Link to repository with training and evaluation scripts
8. Random seeds: Report seeds used; ideally report mean over 3+ seeds

Ethical Considerations

When collecting or using image datasets, consider consent (especially for images of people), geographic and demographic representation, potential for bias amplification, and dual-use concerns. Document the dataset's composition and limitations. Follow the Datasheets for Datasets framework. For generative models, implement safeguards against generating harmful content.