Use this skill when building computer vision applications, implementing image classification, object detection, or segmentation pipelines. Triggers on image classification, object detection, YOLO, semantic segmentation, image preprocessing, data augmentation, transfer learning, CNN architectures, vision transformers, and any task requiring visual recognition or image analysis.
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Use this skill when building computer vision applications, implementing image classification, object detection, or segmentation pipelines. Triggers on image classification, object detection, YOLO, semantic segmentation, image preprocessing, data augmentation, transfer learning, CNN architectures, vision transformers, and any task requiring visual recognition or image analysis.
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Computer Vision
Computer vision enables machines to interpret and reason about visual data - images,
video, and multi-modal inputs. Modern CV pipelines are built on deep neural networks
pretrained on large datasets (ImageNet, COCO, ADE20K) and fine-tuned for specific
domains. PyTorch and its ecosystem (torchvision, timm, ultralytics, albumentations)
cover the full stack from data loading through deployment. Foundation models like
SAM, DINOv2, and OpenCLIP have shifted best practice toward prompt-based and
zero-shot approaches before committing to full training runs.
When to use this skill
Trigger this skill when the user:
Trains or fine-tunes an image classifier on a custom dataset
Runs inference with YOLO, DETR, or other detection models
Builds a semantic or instance segmentation pipeline
Implements data augmentation for CV training
Preprocesses images for model ingestion (resize, normalize, batch)
Exports a vision model to ONNX or optimizes with TensorRT
Evaluates a vision model (mAP, confusion matrix, per-class metrics)
Implements a U-Net, DeepLabV3, or similar segmentation architecture
Do NOT trigger this skill for:
Pure NLP tasks with no visual component (use a language-model skill instead)
3D point-cloud processing or LiDAR-only pipelines (overlap is limited; check domain)
Key principles
Start with pretrained models - Fine-tune ImageNet/COCO weights before training
from scratch. Even a frozen backbone with a new head beats random init on small datasets.
Augment data aggressively - Real-world distribution shifts are unavoidable.
Use albumentations with geometric, color, and noise transforms. Target-aware augments
(mosaic, copy-paste) matter especially for detection.
Validate on representative data - Always hold out data from the exact deployment
distribution. Benchmark on in-distribution AND out-of-distribution splits separately.
Optimize inference separately from training - Training precision (FP32/AMP) and
inference precision (INT8/FP16) have different tradeoffs. Profile, export to ONNX,
then apply TensorRT or OpenVINO post-training quantization.
Monitor for distribution shift - Production images drift from training data
(lighting changes, new object classes, compression artifacts). Log prediction
confidence distributions and trigger retraining pipelines when they degrade.
Core concepts
Task taxonomy
Task
Output
Typical metric
Classification
Single label per image
Top-1 / Top-5 accuracy
Detection
Bounding boxes + labels
mAP@0.5, mAP@0.5:0.95
Semantic segmentation
Per-pixel class mask
mIoU
Instance segmentation
Per-object mask + label
mask AP
Generation / synthesis
New images
FID, LPIPS
Backbone architectures
Backbone
Strengths
Typical use
ResNet-50/101
Stable, well-understood
Classification baseline, feature extractor
EfficientNet-B0..B7
Accuracy/FLOP Pareto front
Mobile + server classification
ViT-B/16, ViT-L/16
Strong with large data, attention maps
High-accuracy classification, zero-shot
ConvNeXt-T/B
CNN with transformer-like training recipe
Drop-in ResNet replacement
DINOv2 (ViT)
Strong self-supervised features
Few-shot, feature extraction
Anchor-free vs anchor-based detection
Anchor-based (YOLOv5, Faster R-CNN) - predefined box aspect ratios per grid cell.
Fast training convergence, tuning required for unusual object scales.
Anchor-free (YOLO11/v8, FCOS, DETR) - predict box center + offsets directly.
Cleaner training, no anchor hyperparameter search, now the default for new projects.
import torch
from torchvision.transforms import v2 as T
from PIL import Image
# Production preprocessing - deterministic, no augmentation
preprocess = T.Compose([
T.Resize((256, 256), interpolation=T.InterpolationMode.BILINEAR, antialias=True),
T.CenterCrop(224),
T.ToImage(),
T.ToDtype(torch.float32, scale=True),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
defload_batch(paths: list[str], device: torch.device) -> torch.Tensor:
"""Load, preprocess, and batch a list of image paths."""
tensors = []
for p in paths:
img = Image.open(p).convert("RGB")
tensors.append(preprocess(img))
return torch.stack(tensors).to(device)
device = torch.device("cuda"if torch.cuda.is_available() else"cpu")
batch = load_batch(["a.jpg", "b.jpg", "c.jpg"], device)
print(batch.shape) # [3, 3, 224, 224]
Deploy a vision model
import torch
import torch.onnx
import onnxruntime as ort
import numpy as np
# --- Export to ONNX ---
model = torch.load("classifier.pth", map_location="cpu")
model.eval()
dummy = torch.randn(1, 3, 224, 224)
torch.onnx.export(
model,
dummy,
"classifier.onnx",
input_names=["image"],
output_names=["logits"],
dynamic_axes={"image": {0: "batch"}, "logits": {0: "batch"}},
opset_version=17,
)
# --- ONNX Runtime inference (CPU or CUDA EP) ---
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
session = ort.InferenceSession("classifier.onnx", providers=providers)
input_name = session.get_inputs()[0].name
definfer_onnx(batch_np: np.ndarray) -> np.ndarray:
return session.run(None, {input_name: batch_np})[0]
# --- TensorRT optimization (requires tensorrt package) ---# Run once offline to build the engine:# trtexec --onnx=classifier.onnx --saveEngine=classifier.trt \# --fp16 --minShapes=image:1x3x224x224 \# --optShapes=image:8x3x224x224 \# --maxShapes=image:32x3x224x224
Evaluate model performance
import torch
import numpy as np
from torchmetrics.classification import (
MulticlassAccuracy,
MulticlassConfusionMatrix,
MulticlassPrecision,
MulticlassRecall,
MulticlassF1Score,
)
from torchmetrics.detection import MeanAveragePrecision
# --- Classification metrics ---defevaluate_classifier(model, loader, num_classes, device):
model.eval()
metrics = {
"acc": MulticlassAccuracy(num_classes=num_classes, top_k=1).to(device),
"prec": MulticlassPrecision(num_classes=num_classes, average="macro").to(device),
"rec": MulticlassRecall(num_classes=num_classes, average="macro").to(device),
"f1": MulticlassF1Score(num_classes=num_classes, average="macro").to(device),
"cm": MulticlassConfusionMatrix(num_classes=num_classes).to(device),
}
with torch.no_grad():
for imgs, labels in loader:
imgs, labels = imgs.to(device), labels.to(device)
preds = model(imgs)
for m in metrics.values():
m.update(preds, labels)
return {k: v.compute() for k, v in metrics.items()}
# --- Detection metrics (COCO mAP) ---
map_metric = MeanAveragePrecision(iou_type="bbox")
# preds and targets follow torchmetrics dict format
preds = [{"boxes": torch.tensor([[10, 20, 100, 200]]), "scores": torch.tensor([0.9]), "labels": torch.tensor([0])}]
tgts = [{"boxes": torch.tensor([[12, 22, 102, 202]]), "labels": torch.tensor([0])}]
map_metric.update(preds, tgts)
result = map_metric.compute()
print(f"mAP@0.5: {result['map_50']:.4f} mAP@0.5:0.95: {result['map']:.4f}")
Implement semantic segmentation
import torch
import torch.nn as nn
from torchvision.models.segmentation import deeplabv3_resnet50, DeepLabV3_ResNet50_Weights
# --- DeepLabV3 fine-tuning ---
NUM_CLASSES = 21# e.g. PASCAL VOC
model = deeplabv3_resnet50(weights=DeepLabV3_ResNet50_Weights.DEFAULT)
model.classifier[4] = nn.Conv2d(256, NUM_CLASSES, kernel_size=1)
model.aux_classifier[4] = nn.Conv2d(256, NUM_CLASSES, kernel_size=1)
# Training stepdefseg_train_step(model, imgs, masks, optimizer, device):
model.train()
imgs, masks = imgs.to(device), masks.long().to(device)
out = model(imgs)
# main loss + auxiliary loss
loss = nn.functional.cross_entropy(out["out"], masks)
loss += 0.4 * nn.functional.cross_entropy(out["aux"], masks)
optimizer.zero_grad()
loss.backward()
optimizer.step()
return loss.item()
# Inference - returns per-pixel class indexdefseg_predict(model, img_tensor, device):
model.eval()
with torch.no_grad():
out = model(img_tensor.unsqueeze(0).to(device))
return out["out"].argmax(dim=1).squeeze(0).cpu() # [H, W]# --- Lightweight U-Net-style architecture (custom) ---classDoubleConv(nn.Module):
def__init__(self, in_ch, out_ch):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1, bias=False),
nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1, bias=False),
nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True),
)
defforward(self, x): returnself.net(x)
classUNet(nn.Module):
def__init__(self, in_channels=3, num_classes=2, features=(64, 128, 256, 512)):
super().__init__()
self.downs = nn.ModuleList()
self.ups = nn.ModuleList()
self.pool = nn.MaxPool2d(2, 2)
ch = in_channels
for f in features:
self.downs.append(DoubleConv(ch, f)); ch = f
self.bottleneck = DoubleConv(features[-1], features[-1] * 2)
for f inreversed(features):
self.ups.append(nn.ConvTranspose2d(f * 2, f, 2, 2))
self.ups.append(DoubleConv(f * 2, f))
self.head = nn.Conv2d(features[0], num_classes, 1)
defforward(self, x):
skips = []
for down inself.downs:
x = down(x); skips.append(x); x = self.pool(x)
x = self.bottleneck(x)
for i inrange(0, len(self.ups), 2):
x = self.ups[i](x)
skip = skips[-(i // 2 + 1)]
if x.shape != skip.shape:
x = torch.nn.functional.interpolate(x, size=skip.shape[2:])
x = self.ups[i + 1](torch.cat([skip, x], dim=1))
returnself.head(x)
Anti-patterns / common mistakes
Anti-pattern
What goes wrong
Correct approach
Training from scratch on small datasets
Model memorizes noise, poor generalization
Always start from pretrained weights; freeze backbone initially
Normalizing with wrong mean/std
Silent accuracy drop when ImageNet stats misapplied to non-ImageNet data
Compute dataset statistics or use the exact stats that match the pretrained model
Leaking augmentation into validation
Inflated validation metrics; surprises in production
Apply only deterministic transforms (resize, normalize) to val/test splits
Skipping anchor/stride tuning for custom scale objects
Model misses very small or very large objects
Analyse object scale distribution; adjust anchor sizes or use anchor-free models
Exporting to ONNX without dynamic axes
Batch-size-1 locked model; crashes on larger batches in production
Always set dynamic_axes for batch dimension (and optionally spatial dims)
Evaluating detection with IoU threshold 0.5 only
Misses regression quality; mAP@0.5:0.95 is 2-3x harder
Report both mAP@0.5 and mAP@0.5:0.95 to COCO convention
Gotchas
Normalizing with wrong mean/std silently degrades accuracy - If you pretrain with ImageNet weights but normalize with different mean/std at inference, predictions silently degrade. The values [0.485, 0.456, 0.406] / [0.229, 0.224, 0.225] are ImageNet-specific; compute your own stats if your data is not RGB photos (e.g., medical images, satellite imagery).
loading="lazy" on the LCP image - This applies to CV deployment: never lazy-load the first above-fold image in a web app. Use fetchpriority="high" on the primary visual.
IV/nonce reuse destroys GCM security - This applies when encrypting model weights or inference results: reusing an IV with the same AES-256-GCM key is catastrophic. Generate fresh randomBytes(12) for every encrypt call.
Augmentation leaking into validation - Applying RandomResizedCrop or ColorJitter to the validation split inflates metrics. Only deterministic transforms (resize, center crop, normalize) belong in the val/test transforms.
ONNX export without dynamic axes locks batch size - Exporting with a fixed batch size of 1 causes runtime crashes in production when the batch size changes. Always set dynamic_axes={"image": {0: "batch"}} during export.
Anchor tuning for unusual object scales - If your objects are very small (satellite imagery, cell microscopy) or very large relative to the image, default YOLO anchor sizes will miss them. Run model.analyze_anchor_fitness() or use anchor-free models for unusual scale distributions.
References
For detailed content on model selection and architecture comparisons, read:
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