Object Detection vs Segmentation: Comparison and Use Cases
When tackling a computer vision problem, choosing the right task and architecture is fundamental. Object Detection, Semantic Segmentation, Instance Segmentation, and Panoptic Segmentation are not interchangeable alternatives: each answers different questions, has different computational requirements, and suits specific use cases. Picking the wrong approach means wasting resources or, worse, failing to solve the actual problem.
In this article we will rigorously compare the main computer vision tasks, with practical PyTorch implementations and concrete guidelines for selecting the right approach in your project.
What You Will Learn
- Fundamental differences between detection, semantic, instance, and panoptic segmentation
- When to use which approach: practical decision tree
- Main architectures for each task and their tradeoffs
- Complete multi-task pipeline implementation in PyTorch
- Evaluation metrics for each task (mAP, mIoU, PQ)
- Speed and accuracy benchmarks on real hardware
- Case studies: autonomous vehicles, medical imaging, retail analytics
1. The Main Computer Vision Tasks
Before comparing approaches, let us precisely define each task with visual examples:
Computer Vision Task Hierarchy
Input image: a street with 3 people and 2 cars
┌─────────────────────────────────────────────────────────────────┐
│ IMAGE CLASSIFICATION: "street with vehicles and people" │
│ Output: 1 label for the entire image │
│ Does NOT say WHERE or HOW MANY objects there are │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ OBJECT DETECTION: 5 bounding boxes │
│ [person(0.95) x1,y1,x2,y2] │
│ [person(0.88) x1,y1,x2,y2] │
│ [person(0.91) x1,y1,x2,y2] │
│ [car(0.97) x1,y1,x2,y2] │
│ [car(0.94) x1,y1,x2,y2] │
│ Knows WHERE and HOW MANY, but not the precise shape │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ SEMANTIC SEGMENTATION: every pixel has a class label │
│ pixel(100,200)="person", pixel(300,400)="car" │
│ Knows the EXACT SHAPE but does NOT separate instances │
│ All "people" = same category, not separate identities │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ INSTANCE SEGMENTATION: mask per object instance │
│ person_1 = {pixels: (100,200),(101,200),...} │
│ person_2 = {pixels: (250,180),(251,180),...} │
│ Knows SHAPE and distinguishes SEPARATE INSTANCES │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ PANOPTIC SEGMENTATION: semantic + instance combined │
│ "things" (countable): instance per person and car │
│ "stuff" (uncountable): semantic for road, sky, buildings │
│ Knows EVERYTHING: shape, class, identity, background │
└─────────────────────────────────────────────────────────────────┘1.1 Detailed Technical Comparison
Computer Vision Task Comparison
| Task | Output | Complexity | Speed | GPU Memory | Metric |
|---|---|---|---|---|---|
| Classification | Label + prob | Low | Very high | Low | Top-1/5 Acc |
| Object Detection | BBox + label | Medium | High | Medium | mAP@0.5 |
| Semantic Seg. | Pixel-label map | Medium-High | Medium | High | mIoU |
| Instance Seg. | BBox + mask | High | Low-Medium | High | mAP@mask |
| Panoptic Seg. | Everything | Very high | Low | Very high | PQ |
2. Object Detection: Architectures and Implementation
2.1 Single-Stage vs Two-Stage Detectors
Single-Stage vs Two-Stage Comparison
| Feature | Single-Stage (YOLO, SSD, RetinaNet) | Two-Stage (Faster R-CNN, Mask R-CNN) |
|---|---|---|
| Pipeline | Single network, direct prediction | RPN proposes regions, then classifies |
| Speed | High (30-150+ FPS) | Low (5-15 FPS) |
| Accuracy | Slightly lower on small objects | Better, especially for small objects |
| Typical use | Real-time, edge, video | Offline analysis, maximum precision |
| Modern examples | YOLO26, RT-DETR, DINO-DETR | Faster R-CNN, Cascade R-CNN, DETR |
Faster R-CNN for High Accuracy Detection
import torch
import torchvision
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
def create_faster_rcnn(num_classes: int) -> torch.nn.Module:
"""
Faster R-CNN with pre-trained ResNet-50 + FPN backbone.
Two-stage: RPN (Region Proposal Network) + classifier.
"""
model = fasterrcnn_resnet50_fpn(
weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT
)
# Replace box predictor for custom number of classes
# +1 because class 0 is reserved for "background"
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes + 1)
return model
def train_faster_rcnn(model, data_loader, num_epochs: int = 10, lr: float = 0.005):
"""Faster R-CNN training loop with built-in loss computation."""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=lr,
momentum=0.9, weight_decay=0.0005)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
for epoch in range(num_epochs):
total_loss = 0.0
for images, targets in data_loader:
images = [img.to(device) for img in images]
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# Faster R-CNN returns a loss dict in training mode
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
optimizer.zero_grad()
losses.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
total_loss += losses.item()
scheduler.step()
print(f"Epoch {epoch+1}/{num_epochs} | Avg Loss: {total_loss/len(data_loader):.4f}")3. Semantic Segmentation with DeepLabv3
Semantic segmentation assigns a class label to every single pixel in the image. It does not distinguish instances: all "people" belong to the same class. Ideal for full scene analysis (autonomous driving, medical analysis, remote sensing).
DeepLabv3 (Chen et al., 2017) uses atrous convolutions (dilated convolutions): convolutions with "holes" that increase the receptive field without increasing parameters, essential for capturing multi-scale context without reducing resolution.
Semantic Segmentation with DeepLabv3
import torch
import torch.nn as nn
import torchvision.models.segmentation as seg_models
from torchvision.models.segmentation import DeepLabV3_ResNet50_Weights
def create_deeplabv3(num_classes: int) -> nn.Module:
"""
DeepLabv3 with pre-trained ResNet-50 backbone.
Uses ASPP (Atrous Spatial Pyramid Pooling) for multi-scale context.
"""
model = seg_models.deeplabv3_resnet50(
weights=DeepLabV3_ResNet50_Weights.DEFAULT
)
# Replace final classifier for custom num_classes
model.classifier[-1] = nn.Conv2d(256, num_classes, kernel_size=1)
model.aux_classifier[-1] = nn.Conv2d(256, num_classes, kernel_size=1)
return model
def train_semantic_segmentation(model, data_loader, num_epochs: int = 20):
"""Training loop for semantic segmentation (mIoU optimization)."""
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
criterion = nn.CrossEntropyLoss(ignore_index=255)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.PolynomialLR(optimizer, total_iters=num_epochs)
for epoch in range(num_epochs):
model.train()
total_loss = 0.0
for images, masks in data_loader:
images = images.to(device)
masks = masks.long().to(device) # [B, H, W], values 0..num_classes-1
outputs = model(images)
main_loss = criterion(outputs['out'], masks)
aux_loss = criterion(outputs['aux'], masks) * 0.4
loss = main_loss + aux_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
scheduler.step()
miou = compute_miou(model, data_loader, device)
print(f"Epoch {epoch+1}/{num_epochs} | Loss: {total_loss/len(data_loader):.4f} | mIoU: {miou:.3f}")
def compute_miou(model, data_loader, device, num_classes: int = 21) -> float:
"""Computes Mean IoU - the standard metric for semantic segmentation."""
model.eval()
intersection = torch.zeros(num_classes, device=device)
union = torch.zeros(num_classes, device=device)
with torch.no_grad():
for images, masks in data_loader:
images, masks = images.to(device), masks.long().to(device)
preds = model(images)['out'].argmax(dim=1)
for cls in range(num_classes):
pred_cls = preds == cls
true_cls = masks == cls
intersection[cls] += (pred_cls & true_cls).sum()
union[cls] += (pred_cls | true_cls).sum()
iou = intersection / (union + 1e-10)
return float(iou[union > 0].mean())4. Instance Segmentation with Mask R-CNN
Instance segmentation combines object detection (bounding box + class) with pixel-level segmentation for each individual instance. Each object has its own independent binary mask. Mask R-CNN (He et al., 2017) extends Faster R-CNN by adding a third parallel "head" for mask prediction.
Mask R-CNN for Instance Segmentation
import torch
import torchvision
from torchvision.models.detection import maskrcnn_resnet50_fpn
from torchvision.models.detection import MaskRCNN_ResNet50_FPN_Weights
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
def create_mask_rcnn(num_classes: int) -> torch.nn.Module:
"""
Mask R-CNN: Faster R-CNN + Mask Head.
Output per instance: bbox + class + 28x28 binary mask.
"""
model = maskrcnn_resnet50_fpn(
weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT
)
# Replace box predictor
in_features_box = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features_box, num_classes + 1)
# Replace mask predictor
in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
model.roi_heads.mask_predictor = MaskRCNNPredictor(
in_features_mask, 256, num_classes + 1
)
return model
def prepare_instance_target(boxes: list, labels: list, masks: list) -> dict:
"""
Prepares the target dict required by Mask R-CNN.
masks: list of boolean arrays [H, W] per instance.
"""
return {
'boxes': torch.tensor(boxes, dtype=torch.float32),
'labels': torch.tensor(labels, dtype=torch.int64),
'masks': torch.tensor(masks, dtype=torch.uint8) # [N, H, W]
}5. Decision Tree: Which Task to Choose?
Task Selection Decision Tree
Question: "What do I need to know about the image?"
|
├─ Just "what objects are present"?
│ └── IMAGE CLASSIFICATION
│ Architectures: ResNet, EfficientNet, ViT
│ Examples: industrial quality gate, content moderation
│
├─ "Where are objects + how many"?
│ └── OBJECT DETECTION
│ │
│ ├─ Need real-time speed (>30 FPS)?
│ │ └── Single-Stage: YOLO26, RT-DETR
│ │
│ └─ Need maximum accuracy (small objects)?
│ └── Two-Stage: Faster R-CNN, DETR
│
├─ "Class of every pixel" (no instance separation)?
│ └── SEMANTIC SEGMENTATION
│ Architectures: DeepLabv3, FCN, SegFormer
│ Examples: road analysis, medical imaging, remote sensing
│
├─ "Separate each object + exact shape"?
│ └── INSTANCE SEGMENTATION
│ Architectures: Mask R-CNN, SOLOv2, YOLACT
│ Examples: object counting, robotics, biology
│
└─ "Everything: separated objects + classified background"?
└── PANOPTIC SEGMENTATION
Architectures: Panoptic FPN, Mask2Former
Examples: full autonomous driving, scene understandingUse Cases by Industry
| Industry | Detection | Semantic Seg. | Instance Seg. | Panoptic |
|---|---|---|---|---|
| Automotive | Pedestrian/vehicle detection | Road/lane segmentation | Separate each pedestrian | Full autonomous scene |
| Medical | Locate lesions in CT | Organ segmentation | Separate each tumor | Full anatomical analysis |
| Retail | Count shelf products | Map planogram zones | Identify each product | Complete shelf analysis |
| Industrial | Detect defects (bounding box) | Classify defective zone | Segment each defect | Full part inspection |
| Agriculture | Count fruits on tree | Segment vegetation | Separate each fruit | Complete field map |
6. Evaluation Metrics for Each Task
Each computer vision task uses specific metrics. Using the wrong metric leads to misleading conclusions: a model with high mAP for detection may have terrible mIoU for segmentation. Understanding these metrics is essential for comparing models and monitoring production systems.
Computing mAP, mIoU, and Panoptic Quality from Scratch
import torch
import numpy as np
from collections import defaultdict
# ---- mAP for Object Detection ----
def compute_ap(recall: np.ndarray, precision: np.ndarray) -> float:
"""
Computes Average Precision (AP) using the 11-point interpolation.
mAP@0.5 = mean of AP for each class at IoU threshold 0.5.
mAP@[0.5:0.95] = mean over thresholds [0.5, 0.55, ..., 0.95]
"""
mrec = np.concatenate(([0.0], recall, [1.0]))
mpre = np.concatenate(([0.0], precision, [0.0]))
# Make precision monotonically decreasing
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# Find recall change points
i = np.where(mrec[1:] != mrec[:-1])[0]
# Area under P-R curve
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return float(ap)
def bbox_iou(box1: np.ndarray, box2: np.ndarray) -> float:
"""
IoU between two boxes in [x1, y1, x2, y2] format.
Used to match predictions to ground truth boxes.
"""
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
intersection = max(0, x2 - x1) * max(0, y2 - y1)
area1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
area2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
union = area1 + area2 - intersection
return intersection / (union + 1e-10)
def compute_map(predictions: list, ground_truths: list,
iou_threshold: float = 0.5,
num_classes: int = 80) -> float:
"""
Compute mAP@IoU_threshold over all classes.
predictions: list of [image_id, class_id, confidence, x1, y1, x2, y2]
ground_truths: list of [image_id, class_id, x1, y1, x2, y2]
"""
# Group by class
class_preds = defaultdict(list)
class_gts = defaultdict(list)
for pred in predictions:
img_id, cls, conf, *box = pred
class_preds[cls].append((img_id, conf, box))
for gt in ground_truths:
img_id, cls, *box = gt
class_gts[cls].append((img_id, box))
aps = []
for cls in range(num_classes):
if cls not in class_gts:
continue
# Sort predictions by confidence (descending)
preds = sorted(class_preds[cls], key=lambda x: -x[1])
gt_by_image = defaultdict(list)
for img_id, box in class_gts[cls]:
gt_by_image[img_id].append({'box': box, 'matched': False})
tp = np.zeros(len(preds))
fp = np.zeros(len(preds))
for i, (img_id, conf, pred_box) in enumerate(preds):
best_iou = 0.0
best_idx = -1
for j, gt in enumerate(gt_by_image[img_id]):
iou = bbox_iou(np.array(pred_box), np.array(gt['box']))
if iou > best_iou:
best_iou = iou
best_idx = j
if best_iou >= iou_threshold and not gt_by_image[img_id][best_idx]['matched']:
tp[i] = 1
gt_by_image[img_id][best_idx]['matched'] = True
else:
fp[i] = 1
cumtp = np.cumsum(tp)
cumfp = np.cumsum(fp)
n_gt = len(class_gts[cls])
recall = cumtp / (n_gt + 1e-10)
precision = cumtp / (cumtp + cumfp + 1e-10)
aps.append(compute_ap(recall, precision))
return float(np.mean(aps)) if aps else 0.0
# ---- mIoU for Semantic Segmentation ----
def compute_miou_fast(preds: torch.Tensor, targets: torch.Tensor,
num_classes: int = 21,
ignore_index: int = 255) -> float:
"""
Fast mIoU computation using confusion matrix.
preds: [B, H, W] - predicted class indices
targets: [B, H, W] - ground truth class indices
"""
mask = targets != ignore_index
preds_flat = preds[mask].long()
targets_flat = targets[mask].long()
# Confusion matrix via bincount
conf_matrix = torch.bincount(
num_classes * targets_flat + preds_flat,
minlength=num_classes ** 2
).reshape(num_classes, num_classes).float()
intersection = conf_matrix.diag()
union = conf_matrix.sum(1) + conf_matrix.sum(0) - intersection
iou = intersection / (union + 1e-10)
# Only average over classes that appear in ground truth
valid = conf_matrix.sum(1) > 0
return float(iou[valid].mean())7. Multi-Task Pipeline: Detection + Segmentation
In many real applications it makes sense to combine multiple tasks in a single architecture for computational efficiency. A practical example: in retail analytics we want to both localize products (detection) and segment the occupied shelf zone (semantic segmentation). A shared backbone reduces total compute by 40-60% compared to two separate models.
Shared Backbone Multi-Task Architecture with FPN
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class MultiTaskModel(nn.Module):
"""
Shared ResNet-50 + FPN backbone with two heads:
- Detection head (anchor-free box prediction)
- Semantic segmentation head (pixel-wise classification)
FPN (Feature Pyramid Network) enables multi-scale feature extraction:
P5 (1/32 resolution) -> large objects
P4 (1/16 resolution) -> medium objects
P3 (1/8 resolution) -> small objects
P2 (1/4 resolution) -> segmentation (highest resolution)
"""
def __init__(self, num_det_classes: int, num_seg_classes: int):
super().__init__()
backbone = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)
self.layer1 = nn.Sequential(backbone.conv1, backbone.bn1,
backbone.relu, backbone.maxpool,
backbone.layer1) # 1/4 resolution
self.layer2 = backbone.layer2 # 1/8
self.layer3 = backbone.layer3 # 1/16
self.layer4 = backbone.layer4 # 1/32
# FPN lateral connections (1x1 convolutions to normalize channels)
self.fpn = nn.ModuleDict({
'p5': nn.Conv2d(2048, 256, 1),
'p4': nn.Conv2d(1024, 256, 1),
'p3': nn.Conv2d(512, 256, 1),
'p2': nn.Conv2d(256, 256, 1),
})
# Detection head on P3 (best for medium/small objects)
self.det_head = nn.Sequential(
nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(inplace=True),
nn.Conv2d(256, num_det_classes * 5, 1) # 4 bbox coords + 1 objectness
)
# Segmentation head on P2 (highest resolution) with decoder
self.seg_head = nn.Sequential(
nn.Conv2d(256, 128, 3, padding=1), nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 64, 4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(64, 32, 4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(32, num_seg_classes, 1)
)
def forward(self, x: torch.Tensor) -> dict:
# Bottom-up pathway (backbone)
c2 = self.layer1(x) # 1/4
c3 = self.layer2(c2) # 1/8
c4 = self.layer3(c3) # 1/16
c5 = self.layer4(c4) # 1/32
# Top-down pathway with lateral connections (FPN)
p5 = self.fpn['p5'](c5)
p4 = self.fpn['p4'](c4) + F.interpolate(p5, scale_factor=2)
p3 = self.fpn['p3'](c3) + F.interpolate(p4, scale_factor=2)
p2 = self.fpn['p2'](c2) + F.interpolate(p3, scale_factor=2)
# Task-specific heads
det_out = self.det_head(p3)
seg_out = self.seg_head(p2)
seg_out = F.interpolate(seg_out, size=x.shape[-2:],
mode='bilinear', align_corners=False)
return {'detection': det_out, 'segmentation': seg_out}
def compute_multitask_loss(outputs: dict, det_targets, seg_targets,
det_weight: float = 1.0,
seg_weight: float = 0.5) -> tuple:
"""
Balanced multi-task loss.
det_weight and seg_weight control the relative importance of each task.
These values must be tuned: if the detection loss is 100x larger than
the segmentation loss, increase seg_weight proportionally.
"""
det_loss = nn.BCEWithLogitsLoss()(outputs['detection'], det_targets)
seg_loss = nn.CrossEntropyLoss(ignore_index=255)(
outputs['segmentation'], seg_targets
)
total = det_weight * det_loss + seg_weight * seg_loss
return total, {'det': float(det_loss), 'seg': float(seg_loss)}7.1 Advanced Multi-Task Loss Balancing
A common challenge in multi-task learning is loss scale imbalance: the detection loss might be 100x larger than the segmentation loss, causing the optimizer to focus entirely on detection and ignore segmentation. Uncertainty weighting (Kendall et al., 2018) automatically balances losses by learning task-specific uncertainty weights.
Uncertainty-Weighted Multi-Task Loss (Kendall et al., 2018)
import torch
import torch.nn as nn
class UncertaintyWeightedLoss(nn.Module):
"""
Automatic loss balancing using learned uncertainty weights.
From: "Multi-Task Learning Using Uncertainty to Weigh Losses"
(Kendall et al., CVPR 2018)
L_total = sum_i [ 1/(2*sigma_i^2) * L_i + log(sigma_i) ]
sigma_i (log_sigma_i) is a learnable parameter per task.
It automatically balances the losses without manual tuning.
"""
def __init__(self, n_tasks: int):
super().__init__()
# Initialize log_sigma to 0 (sigma = 1, no initial weighting)
self.log_sigmas = nn.Parameter(torch.zeros(n_tasks))
def forward(self, losses: list) -> tuple:
assert len(losses) == len(self.log_sigmas)
weighted_losses = []
for loss, log_sigma in zip(losses, self.log_sigmas):
# L_weighted = loss / (2 * sigma^2) + log(sigma)
# Numerically stable: use log_sigma instead of sigma
precision = torch.exp(-2 * log_sigma) # 1 / sigma^2
weighted_losses.append(0.5 * precision * loss + log_sigma)
total = sum(weighted_losses)
weights = [float(torch.exp(-2 * ls)) for ls in self.log_sigmas]
return total, weights
# Usage in training:
# uncertainty_loss = UncertaintyWeightedLoss(n_tasks=2)
# optimizer = torch.optim.AdamW(
# list(model.parameters()) + list(uncertainty_loss.parameters()),
# lr=1e-4
# )
# ...
# total, weights = uncertainty_loss([det_loss, seg_loss])
# print(f"Det weight: {weights[0]:.3f}, Seg weight: {weights[1]:.3f}")8. Best Practices and Performance Benchmarks
COCO Benchmark (2025)
| Model | Task | mAP/mIoU | FPS (V100) | Params |
|---|---|---|---|---|
| YOLO26m | Detection | 57.2 mAP | 100+ | 25M |
| Faster R-CNN R50 | Detection | 40.2 mAP | 18 | 41M |
| DeepLabv3 R50 | Semantic Seg. | 74.3 mIoU | 45 | 39M |
| SegFormer-B5 | Semantic Seg. | 83.1 mIoU | 15 | 85M |
| Mask R-CNN R50 | Instance Seg. | 36.1 mAP | 14 | 44M |
| Mask2Former R50 | Panoptic | 51.9 PQ | 8 | 44M |
Common Design Mistakes
- Using segmentation when detection suffices: If you just need to count or localize objects, use detection. Segmentation is far more expensive to annotate and train.
- Ignoring real-time requirements: Mask R-CNN at 14 FPS is unacceptable for a live surveillance system. Always choose architecture based on latency requirements.
- Unbalanced datasets for segmentation: If one class covers 95% of pixels (e.g., background), the model will trivially predict it. Use weighted loss or class-balanced sampling.
- Confusing mIoU and mAP: These are different metrics. mIoU measures pixel-level precision (segmentation); mAP measures bounding box quality (detection).
- Multi-task without loss balancing: In multi-task architectures, loss values from different tasks can have very different scales. Use gradient normalization or uncertainty weighting.
Conclusions
We explored the full spectrum of computer vision tasks, from their fundamental differences to practical implementations, evaluation metrics, and advanced multi-task architectures:
- Classification, Detection, Semantic/Instance/Panoptic Segmentation each have distinct outputs, costs, and use cases - always choose based on the actual question being asked
- YOLO26 excels at real-time detection; Faster R-CNN at offline high accuracy where every false negative is costly
- DeepLabv3 and SegFormer for semantic segmentation; Mask R-CNN adds instance separation at the cost of speed
- mAP measures bounding box quality; mIoU measures pixel-level accuracy; PQ combines both for panoptic tasks
- Multi-task architectures with shared FPN backbone save 40-60% compute vs separate models
- Uncertainty-weighted loss (Kendall et al.) automatically balances multi-task training without manual tuning
- The decision tree guides selection of the right approach for each business problem and latency requirement