# Widzenie komputerowe

EN: **Computer vision** · id `visionnode`

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › [Język, obraz, audio](https://aiatlasgraph.com/t/modalities.md) › Widzenie komputerowe

## Description

- PL: Rozpoznawanie zawartości i struktury obrazu oraz wideo: co jest na obrazie, gdzie to jest, jaki ma kształt i jak się porusza. Generowanie obrazu, 3D i modele obraz–tekst mają osobne węzły obok.
- EN: Recognising content and structure in images and video: what is in the picture, where it is, what shape it has and how it moves. Image generation, 3D and vision–language models have their own sibling nodes.

## In one sentence

- PL: To uczenie programów rozpoznawania, co widać na zdjęciach i filmach, gdzie to jest i jak się porusza.
- EN: This teaches software to recognise what appears in photos and videos, where it is and how it moves.

## Problem it solves

- PL: Nie możesz ręcznie obejrzeć wszystkich zdjęć ani stale śledzić obrazu z kamer.
- EN: You cannot inspect every photo by hand or watch camera feeds constantly.

## What it does

- PL: Model łączy wzory kolorów i kształtów z poznanymi obiektami, a kolejne klatki pozwalają mu analizować ruch.
- EN: A model connects colour and shape patterns to familiar objects, while successive frames let it analyse movement.

## Everyday example

- PL: Kamera nad taśmą produkcyjną pomaga wykryć uszkodzone opakowanie i wskazuje jego położenie.
- EN: A camera above a conveyor helps detect a damaged package and shows its position.

## This concerns you if you…

- PL: przeglądasz dużo zdjęć lub filmów
- PL: potrzebujesz automatycznego sprawdzania obrazu z kamery
- EN: review many photos or videos
- EN: need automated checks of camera images

## Keywords

ImageNet · COCO · ResNet · Vision Transformer · feature maps · bounding boxes · pixel masks · keypoint heatmaps · optical flow · transfer learning

## How it works

- PL: Enkoder wydobywa cechy przestrzenne z pikseli, a głowice zadaniowe mapują je na etykiety, ramki, maski lub ruch. Trening wiąże te predykcje z anotacjami albo sygnałem samonadzorowanym i kontroluje zachowanie przy zmianach obrazu.
- EN: An encoder extracts spatial features from pixels, and task heads map them to labels, boxes, masks or motion. Training links predictions to annotations or self-supervised signals and tests behaviour under image changes.

## Limits

- PL: Oświetlenie, perspektywa i zasłonięcie zmieniają reprezentację sceny. Drobne szczegóły mogą ginąć w skalowaniu, a dobre wyniki dla jednej domeny nie gwarantują transferu.
- EN: Lighting, viewpoint and occlusion change scene representations. Scaling can lose fine details, and strong results in one domain do not guarantee transfer.

## Key terms

- PL: **mapa cech**: Przestrzenna reprezentacja obrazu wyliczona przez enkoder.
- PL: **głowica zadaniowa**: Moduł przekształcający cechy w wynik wybranego zadania.
- EN: **feature map**: A spatial image representation computed by an encoder.
- EN: **task head**: A module converting features into a chosen task's outputs.

## Know first

- [Sieci konwolucyjne i sekwencyjne (Convolutional and recurrent networks)](https://aiatlasgraph.com/t/cnn.md)
- [Uczenie nadzorowane (Supervised learning)](https://aiatlasgraph.com/t/supervised.md)

## Examples

- PL: Klasyfikacja · detekcja · segmentacja · poza · wideo · ViT
- EN: Classification · detection · segmentation · pose · video · ViT

## Subtopics (8 direct, 20 in total)

- [Detekcja obiektów (Object detection)](https://aiatlasgraph.com/t/objectdetection.md): Finding objects in an image: a bounding box plus a class for each object. Usually evaluated with IoU and mAP.
  - [Detekcja obiektów opisanych tekstem (Open-vocabulary object detection)](https://aiatlasgraph.com/t/openvocabdetection.md): Detecting objects queried by names or natural-language descriptions rather than one fixed closed label set; includes zero-shot generalisation to new categories.
  - [Detekcja z transformerem (DETR) (Transformer-based object detection (DETR))](https://aiatlasgraph.com/t/detrdetectors.md): Detection as direct set prediction by a transformer, without hand-designed anchors or NMS post-processing.
  - [Detektory jednoetapowe (YOLO) (Single-stage object detectors)](https://aiatlasgraph.com/t/onestagedetectors.md): The model predicts boxes and classes without a separate region-proposal stage. The YOLO family shows that such detectors can run in real time and on edge devices.
- [Estymacja pozy i punktów kluczowych (Pose and keypoint estimation)](https://aiatlasgraph.com/t/poseestimation.md): Detecting keypoints (joints, hands, face) and body skeletons in 2D or 3D — the basis of motion analysis, sports and gesture control.
- [Klasyfikacja obrazów (Image classification)](https://aiatlasgraph.com/t/imageclassification.md): Assigning one or more labels to a whole image (e.g. “cat”, “defective product”). It is one of the foundational computer-vision tasks.
- [Modele bazowe dla obrazu (Vision backbones and foundation models)](https://aiatlasgraph.com/t/visionbackbones.md): Pretrained vision models that extract image features reusable for classification, detection and segmentation.
  - [Dostrajanie modeli wizyjnych do własnych danych (Fine-tuning vision models on custom data)](https://aiatlasgraph.com/t/visiontransfer.md): Adapting a pretrained model to a custom dataset by fine-tuning the whole network, selected layers, or a new task head.
  - [Samonadzorowane uczenie z obrazów (Self-supervised visual pretraining)](https://aiatlasgraph.com/t/visionssl.md): Training a vision model on unlabelled images — e.g. by reconstructing masked patches or comparing different views of the same image.
  - [Vision Transformer (ViT)](https://aiatlasgraph.com/t/vit.md): A transformer applied to an image split into patches treated as tokens; a major family of vision architectures alongside convolutional networks.
  - [Wspólna przestrzeń obraz–tekst (CLIP) (Contrastive image–text pretraining (CLIP))](https://aiatlasgraph.com/t/clipcontrastive.md): Learning joint embeddings of images and captions from image–text pairs; enables zero-shot classification and image search by text description.
- [Powiększanie i naprawa zdjęć (super-rozdzielczość) (Image upscaling and restoration (super-resolution))](https://aiatlasgraph.com/t/imagerestoration.md): Recovering a clean, higher-resolution image from a degraded one (low resolution, noise, blur, compression). Approaches range from GAN models trained on synthetic degradations to diffusion-based restoration with a generative prior, which can add plausible but not necessarily true detail.
- [Rozpoznawanie tekstu na obrazach (OCR) (Optical character recognition (OCR))](https://aiatlasgraph.com/t/ocr.md): Converting images of print, handwriting or scene text into machine text; usually line or word detection plus recognition.
- [Rozumienie wideo (Video understanding)](https://aiatlasgraph.com/t/videounderstanding.md): Analysing frame sequences: adds time, motion and far more data than a single image.
  - [Rozpoznawanie akcji w wideo (Action recognition)](https://aiatlasgraph.com/t/actionrecognition.md): Recognising what happens in a clip (running, falling, passing a ball) from appearance and motion over time.
  - [Śledzenie obiektów w wideo (Multi-object tracking)](https://aiatlasgraph.com/t/objecttracking.md): Keeping the identity of the same objects across frames, usually by linking detector outputs frame to frame (tracking-by-detection).
- [Segmentacja obrazu (Image segmentation)](https://aiatlasgraph.com/t/segmentation.md): Dividing an image into regions at pixel level — masks instead of boxes.
  - [Segmentacja instancji i panoptyczna (Instance and panoptic segmentation)](https://aiatlasgraph.com/t/instanceseg.md): A separate mask for each object (instances); the panoptic variant unifies objects and background in one consistent map.
  - [Segmentacja na wskazanie (Segment Anything) (Promptable segmentation)](https://aiatlasgraph.com/t/promptseg.md): A general model returns a mask for an object indicated by a click, a box or a short text phrase — no class-specific training; newer versions also work on video.
  - [Segmentacja semantyczna (Semantic segmentation)](https://aiatlasgraph.com/t/semanticseg.md): Every pixel gets a class label (road, sky, person) without separating individual instances.

## Related topics

Untyped, symmetric "see also" links.

- [Dane przestrzenne i 3D (Spatial and 3D perception)](https://aiatlasgraph.com/t/spatial.md)
- [Mapy istotności dla sieci neuronowych (Saliency maps and gradient attribution)](https://aiatlasgraph.com/t/saliencymaps.md)
- [Przetwarzanie dokumentów (Document AI)](https://aiatlasgraph.com/t/docai.md)

## Sources

- [Hugging Face · Audio Course](<https://huggingface.co/learn/audio-course/en/chapter0/introduction>) `audio` · reference: Dane audio, klasyfikacja, ASR, TTS i modele mowy.
- [Stanford CS231n · Computer Vision (2026)](<https://cs231n.stanford.edu/>) `vision` · reference: Sieci neuronowe i rozumienie obrazów.
- [Stanford CS336 · Language Modeling from Scratch (2025)](<https://cs336.stanford.edu/spring2025/>) `cs336` · reference: Budowa modeli, dane, trening, systemy, inferencja i post-training.
- [DeepLearning.AI — course catalogue](<https://www.deeplearning.ai/courses>) `dlai` · course catalogue: 42 topic labels with counts, about 130 courses.
- [Stanford CS231n: Deep Learning for Computer Vision — lecture schedule (Spring 2026)](<https://cs231n.stanford.edu/schedule.html>) `cs231n-2026-schedule` · by Stanford University (CS231n)
- [Hugging Face · Community Computer Vision Course — syllabus](<https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome>) `hf-cv-course` · by Hugging Face i społeczność

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/visionnode · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30