OneFormer (fastforks)
OneFormer (Jain et al. 2023): Mask2Former-style universal segmentation conditioned on a task token; Swin backbone, run here for the semantic task.
This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: https://github.com/kleinicke/fastforks. Kernels for the kernels backend: https://huggingface.co/fastforks/kernels. Original code: https://github.com/huggingface/transformers at c4c4605f9618.
Author weight repos: https://huggingface.co/shi-labs/oneformer_ade20k_swin_large.
Task: segmentation. Tier: stable. Outputs: logits. Backends: portable, kernels.
Input: one RGB image. Output: per-pixel class logits.
Variants
Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in card.toml.
| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
oneformer-swinl-ade20k@1 |
oneformer-swinl-ade20k.config.json oneformer-swinl-ade20k.bin |
27452b656a46c0b2fe11dfec |
MIT | yes |
Licence
Original-code strings, recorded as they are (research-use package; check them before any other use). Code: Apache-2.0. Files: .
Training data: ImageNet-21k / ImageNet-22k (full ImageNet, fall 2011 release), ADE20K (SceneParsing, 150 classes).
Licence evidence: https://huggingface.co/shi-labs/oneformer_ade20k_swin_large/blob/4a5bac8e64f82681a12db2e151a4c2f4ce6092b2/README.md
Input size
any H x W >= 1, resized or padded inside the model
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): oneformer-swinl-ade20k 1988x2880 (8.4 GiB peak allocated, 14.9 reserved)
Speed and memory
Segmentation, synthetic, 1024x2048
| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
|---|---|---|---|---|---|---|---|---|---|
oneformer-swinl-ade20k |
307.22 | 205.1 | 139.06 | 99.95 | 2.1x | 1.4x | 7395 / 7792 | 2054 / 2476 | 2054 / 2476 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
oneformer-swinl-ade20k |
5.08 (512x512) | 4.10 | 3.27† | 0.81 (512x512) | 0.15† | 0.11† |
torch.compile and TensorRT
| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
|---|---|---|---|---|---|---|---|---|---|---|
oneformer-swinl-ade20k |
1024x2048 | 139.06 | 138.6 | 0.4 | fails: ONNX export: conversion failed | – | 99.95 | 2054 / 2476 | 2046 / 2474 | – |
Fixed-size speed and peak memory, all models
| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
|---|---|---|---|---|---|
oneformer-swinl-ade20k |
portable | 86.2 ms / 1295 / 1940 | 120.8 ms / 2220 / 3960 | 138.9 ms / 3772 / 6702 | OOM |
oneformer-swinl-ade20k |
kernels | 59.0 ms / 1295 / 1858 | 85.4 ms / 2222 / 3854 | 103.3 ms / 3772 / 6642 | OOM |
Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
Hardware, protocol and caveats: benchmarks (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or (fp32) where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
Accuracy
Parity with the original code
Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.001 and p99 <= 0.01 (absolute error of the output logits).
Usage
from fastforks import segmentation
model = segmentation.load("oneformer-swinl-ade20k") # mode="auto": the preferred backend from recorded measurements
result = model("photo.jpg")
result.labels # [H, W] per-pixel class index
result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
Install with pip install fastforks (see https://github.com/kleinicke/fastforks). Other variants: oneformer-swinl-ade20k.
Kernels backend
segmentation.load("oneformer-swinl-ade20k", mode="kernels") runs our CUDA kernels (fvm_seg, seg_up). It needs a prebuilt kernel for your GPU architecture (downloaded from https://huggingface.co/fastforks/kernels on first use) and a recent NVIDIA driver; if there is none it raises BackendUnavailableError with the reason and never falls back silently. mode="auto" picks kernels when it can run and logs the choice.