# Pictograph-SceneParse150-2XL

Pictograph-SceneParse150-2XL is a public instance segmentation model by Pictograph Research on Pictograph, trained on the ADE-20K dataset and exportable to ONNX for inference. Reported mAP of 0.2826511859893799.

## Overview

- Type: instance segmentation model
- Task: Segmentation (https://pictograph.io/explore/task/segmentation)
- Architecture: RF-DETR Seg 2Xlarge
- Owner: Pictograph Research
- Trained on: ADE-20K (https://pictograph.io/datasets/pictograph-research/ade-20k)
- License: BSD 3-Clause License (https://opensource.org/license/bsd-3-clause)
- Export: ONNX
- Stars: 0
- Page: https://pictograph.io/models/pictograph-research/pictograph-sceneparse150-2xl
- Hugging Face: https://huggingface.co/pictograph/pictograph-sceneparse150-2xl

## Evaluation metrics

- mAP: 28.3%
- mAP50: 43.3%

## About

# Pictograph-SceneParse150-2XL

> An instance segmentation model (RF-DETR Seg 2Xlarge), trained on 150 classes, scoring 28.3% mAP.

**[Pictograph Research](/org/pictograph-research)** · Instance Segmentation · RF-DETR Seg 2Xlarge · FP32 · [BSD-3-Clause](https://opensource.org/license/bsd-3-clause) · Public

## Overview

This is an instance segmentation model built on **RF-DETR Seg 2Xlarge**, trained on the [ADE-20K](/datasets/pictograph-research/ade-20k) dataset. It expects **768x768** input and runs in FP32 precision. It predicts wall, building, sky, floor, tree, ceiling, and 144 more.

## Performance

Headline: **28.3% mAP**.

| Metric | Value |
| :--- | ---: |
| mAP | 28.3% |
| mAP@50 | 43.3% |

## Classes

Class index matches the model's output order.

| # | Class |
| ---: | :--- |
| 0 | wall |
| 1 | building |
| 2 | sky |
| 3 | floor |
| 4 | tree |
| 5 | ceiling |
| 6 | road |
| 7 | bed |
| 8 | windowpane |
| 9 | grass |
| 10 | cabinet |
| 11 | sidewalk |
| 12 | person |
| 13 | earth |
| 14 | door |
| 15 | table |
| 16 | mountain |
| 17 | plant |
| 18 | curtain |
| 19 | chair |
| 20 | car |
| 21 | water |
| 22 | painting |
| 23 | sofa |
| 24 | shelf |
| 25 | house |
| 26 | sea |
| 27 | mirror |
| 28 | rug |
| 29 | field |
| 30 | armchair |
| 31 | seat |
| 32 | fence |
| 33 | desk |
| 34 | rock |
| 35 | wardrobe |
| 36 | lamp |
| 37 | bathtub |
| 38 | railing |
| 39 | cushion |
| 40 | base |
| 41 | box |
| 42 | column |
| 43 | signboard |
| 44 | chest_of_drawers |
| 45 | counter |
| 46 | sand |
| 47 | sink |
| 48 | skyscraper |
| 49 | fireplace |
| 50 | refrigerator |
| 51 | grandstand |
| 52 | path |
| 53 | stairs |
| 54 | runway |
| 55 | case |
| 56 | pool_table |
| 57 | pillow |
| 58 | screen_door |
| 59 | stairway |
| 60 | river |
| 61 | bridge |
| 62 | bookcase |
| 63 | blind |
| 64 | coffee_table |
| 65 | toilet |
| 66 | flower |
| 67 | book |
| 68 | hill |
| 69 | bench |
| 70 | countertop |
| 71 | stove |
| 72 | palm |
| 73 | kitchen_island |
| 74 | computer |
| 75 | swivel_chair |
| 76 | boat |
| 77 | bar |
| 78 | arcade_machine |
| 79 | hovel |
| 80 | bus |
| 81 | towel |
| 82 | light |
| 83 | truck |
| 84 | tower |
| 85 | chandelier |
| 86 | awning |
| 87 | streetlight |
| 88 | booth |
| 89 | television_receiver |
| 90 | airplane |
| 91 | dirt_track |
| 92 | apparel |
| 93 | pole |
| 94 | land |
| 95 | bannister |
| 96 | escalator |
| 97 | ottoman |
| 98 | bottle |
| 99 | buffet |
| 100 | poster |
| 101 | stage |
| 102 | van |
| 103 | ship |
| 104 | fountain |
| 105 | conveyer_belt |
| 106 | canopy |
| 107 | washer |
| 108 | plaything |
| 109 | swimming_pool |
| 110 | stool |
| 111 | barrel |
| 112 | basket |
| 113 | waterfall |
| 114 | tent |
| 115 | bag |
| 116 | minibike |
| 117 | cradle |
| 118 | oven |
| 119 | ball |
| 120 | food |
| 121 | step |
| 122 | tank |
| 123 | trade_name |
| 124 | microwave |
| 125 | pot |
| 126 | animal |
| 127 | bicycle |
| 128 | lake |
| 129 | dishwasher |
| 130 | screen |
| 131 | blanket |
| 132 | sculpture |
| 133 | hood |
| 134 | sconce |
| 135 | vase |
| 136 | traffic_light |
| 137 | tray |
| 138 | ashcan |
| 139 | fan |
| 140 | pier |
| 141 | crt_screen |
| 142 | plate |
| 143 | monitor |
| 144 | bulletin_board |
| 145 | shower |
| 146 | radiator |
| 147 | glass |
| 148 | clock |
| 149 | flag |

## Training

| Setting | Value |
| :--- | :--- |
| Base dataset | [ADE-20K](/datasets/pictograph-research/ade-20k) |
| Export | ADE-20K-base |
| Dataset size | 22,207 images |
| Input size | 768x768 |
| Epochs | 12 |
| Batch size | 4 |
| Learning rate | 0.0002 |
| Model size | 2xlarge |
| Precision | FP32 |
| Version | 1.0.0 |

## Use this model

Run inference with the Pictograph SDK:

```python
from pictograph import Client, get_model, InstanceSegmentationModel, InstanceSegmentationResult

client = Client(api_key="pk_live_...")  # Settings -> API Keys

# Downloads + caches the weights on first use
model: InstanceSegmentationModel = get_model(
    name="Pictograph-SceneParse150-2XL",
    task="instance_segmentation",
    format="onnx",
    precision="fp32",
    device="cpu",
    client=client,
)
result: InstanceSegmentationResult = model.predict(image="photo.jpg")
print(result)
```

```python
# Fully offline (no API key) once you have the weights + config.json
from pictograph import load_model, InstanceSegmentationModel, InstanceSegmentationResult

model: InstanceSegmentationModel = load_model(
    "/path/to/weights.onnx",
    "/path/to/config.json",
    task="instance_segmentation",
    format="onnx",
)
result: InstanceSegmentationResult = model.predict(image="photo.jpg")
```

- Full inference guide: [local inference docs](https://pictograph.io/docs/local-inference).
- REST + CLI: [models API reference](https://pictograph.io/docs/api-reference/models).
- Deploy it as an always-on endpoint: [deployments](https://pictograph.io/docs/deployments).

## License

Released under [BSD-3-Clause](https://opensource.org/license/bsd-3-clause).


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Free to view. Sign in at https://app.pictograph.io to fork this model, run batch inference, or deploy it as a live endpoint.