...understand images and video
Jump to navigation
Jump to search
↺ Start over · Full treeYou are here: AI Solver › Understand unstructured data › ...understand images and video
| AI Solver map |
|---|
|
Your current page is in bold
0 · Start
1 · Predict & analyze
tables, numbers, events
Predict values
Predict categories Forecast time series Cluster / segment Find outliers Make recommendations Find key drivers 2 · Understand unstructured data
text, documents, images, audio
3 · Generate & converse
4 · Decide & act
5 · Build it right
applies to every path
|
YouTube ... Quora ...Google search ...Google News ...Bing News
- AI Solver ... Image Classification ... Neural Network ... Embedding ... ...find outliers ... ...deploy AI
Use this page when your data is pictures or video: products on a line, crops in a field, X-rays, store shelves, scanned forms, camera feeds. Start from a pretrained model and adapt it with a modest number of your own labeled images.
Start here
- What do you need from each image?
- One label for the whole image (defective or not, species, document type) → fine-tune a pretrained backbone (ViT, ConvNeXt, EfficientNet) → Image Classification; very few labels → zero-shot with image–text embeddings (CLIP or SigLIP style)
- Where things are (boxes, counts) → object detection: the YOLO family (Ultralytics) or RT-DETR. Classes change often or are described in words → open-vocabulary detectors (Grounding DINO, OWLv2, YOLO-World)
- Exact outlines (pixel masks) → segmentation: Segment Anything (SAM 2 and SAM 3, promptable with clicks, boxes or text, and able to track objects in video) or Mask2Former; medical imaging → specialized models
- Read text in images → OCR (Tesseract, PaddleOCR) or a vision-language model; forms and documents → ...understand language
- Describe, answer questions, check rules ("is a hard hat worn?") → vision-language models (VLMs): GPT, Gemini and Claude, or open models such as Qwen-VL and Gemma
- Follow things through video (counting, tracking, events) → detector + tracker (e.g., ByteTrack); video-capable VLMs for summaries and search
- Body pose or gestures → keypoint models (MediaPipe, YOLO pose)
- Depth or 3D → monocular depth models (e.g., Depth Anything) or 3D reconstruction (Gaussian splatting)
- Find rare defects with few bad examples → anomaly detection on images: ...find outliers
- Constraints
- Real time on a camera, robot or phone → small detector variants, quantization, edge NPUs/GPUs → ...deploy AI
- Few labeled images → pre-label with SAM or a VLM, have people correct the labels (active learning)
- Faces or biometrics → check the law first. The EU AI Act bans untargeted scraping of facial images and, with narrow exceptions, real-time remote biometric identification in public spaces for law enforcement; get consent and legal review.
Quick picks
| Need | Try first | Then compare |
|---|---|---|
| Image label | Fine-tuned pretrained classifier | Zero-shot CLIP/SigLIP, a VLM |
| Boxes and counts | YOLO (Ultralytics) | RT-DETR, open-vocabulary detector |
| Pixel masks | SAM 2 / SAM 3 | Mask2Former, U-Net (medical) |
| Read text | PaddleOCR / cloud OCR | VLM-based OCR |
| Describe or check a scene | Vision-language model | Detector + rules (cheaper, more predictable) |
| Defects with few examples | PatchCore / PaDiM | Fine-tuned classifier once you have examples |
Real-world examples
- Targeted weed spraying — John Deere's See & Spray uses boom-mounted cameras and machine learning to spot weeds and fire individual nozzles. Deere reported average herbicide savings of 59% on more than 1 million acres in 2024, and nearly 50% across more than 5 million acres in 2025. See & Spray customers see 59% average herbicide savings | John Deere ... 2025 season results | Farms.com
Watch out
- Lab to field gap — lighting, camera angle and seasons change; collect test images from the real setting.
- Label quality matters more than model choice; audit a sample of labels.
- Privacy — blur faces and plates when you do not need them.
Measure success
mAP (detection), IoU or Dice (segmentation), top-1 accuracy and per-class recall (classification), character or field accuracy (OCR), plus frames per second and latency on the target device. More: ...evaluate and trust AI
Learn more
- Ultralytics YOLO documentation
- Computer vision tasks and models | Hugging Face
- EU AI Act overview | European Commission
- Image Classification ... Neural Network ... Signal Processing
Where next? ◀ Understand unstructured data · ...understand language · ...find outliers · ...deploy AI · ▶ ...evaluate and trust AI