---
title: Estimate depth, segment and detect
description: "The three image verbs: per-pixel depth maps, semantic segmentation masks, and open-vocabulary detection, from the command line and over HTTP."
---

The vision families answer three per-image questions: how far away everything is (`depth`), what class each pixel belongs to (`segment`), and where the named things are (`detect`). The three verbs share their media handling (any image format the decoder accepts, one artifact per input) and their serving story, so this guide works all three.

## Estimate depth

A depth family (Depth Anything and its relatives) maps each pixel to distance:

```bash
clikart-cli depth-anything/Depth-Anything-V2-Small-hf depth photo.jpg
```

```text
photo.jpg: 64x64 min=1.877290 max=4.294917
```

The payload is one line per image: the map's extent and its value range (relative maps: larger means closer). Artifacts opt in with `--output <dir>`, one per image, and each payload line then ends with ` -> <path>`. The default `--output-format raw` writes the Float32 map as `<stem>_depth.npy`, the form a downstream consumer wants; `--output-format colormap` renders `<stem>_depth.png` instead, and `--colormap` picks its lookup table (`spectral` or `gray`).

## Segment an image

A segmentation family labels every pixel with a class:

```bash
clikart-cli facebook/detr-resnet-50-panoptic segment photo.jpg
```

The payload is the class-mask document: which classes appear, their pixel share, and the rendered overlay's path. `--mask-output mask.npy` also writes the raw class-id mask (UInt8, `[H, W]`) as a sidecar for programmatic consumers, and `--confidence` adds per-label confidence to the listing.

## Detect, with an open vocabulary or a trained class set

The open-vocabulary detectors (OWL-ViT, Grounding DINO) find objects you NAME at run time through `--labels`; the closed-set DETR line (DETR, Deformable DETR, D-FINE, RF-DETR, RT-DETR) detects its checkpoint's trained classes and takes no label prompt. The open-vocabulary form:

```bash
clikart-cli google/owlvit-base-patch32 detect photo.jpg \
    --labels "a red bicycle, a street sign" --confidence 0.3
```

```text
label            score   box (x1, y1, x2, y2)
a red bicycle    0.812   (118, 204, 371, 468)
a street sign    0.644   (402, 61, 455, 152)
```

One row per hit, in score order. The labels encode once and every image scores against them, so a many-image sweep pays the text encoding a single time; `--max-detections` caps the rows per image.

## Over HTTP

Each verb has its serving twin in [the route table](serve-openai-compatible.md): `POST /v1/depth` (multipart image in, depth artifacts out), `POST /v1/segmentations` (image in, class-mask document out), and `POST /v1/detections` (image plus an open-vocabulary `prompt` in, box/score/label rows out):

```bash
clikart-cli google/owlvit-base-patch32 serve --port 8000
```

```bash
curl -s http://127.0.0.1:8000/v1/detections \
  -F image=@photo.jpg -F prompt="a red bicycle, a street sign"
```

The operational story (binding, health, admission control) is the serve guide's, unchanged. Model sizing lives in [Model requirements](../model-requirements.md); the embedding-side image comparisons (which image is LIKE which, rather than what is IN one) are [Embed, compare and rerank](embed-and-rerank.md).