OpenWorldSAM extends SAM2 with language understanding to enable open-vocabulary instance, panoptic, semantic, and referring segmentation from arbitrary text prompts — without any task-specific fine-tuning.

This repository is an unofficial HuggingFace Hub mirror of the GinnyXiao/OpenWorldSAM checkpoint, uploaded to enable loading via transformers and the FiftyOne Model Zoo. All credit belongs to the original authors.

Attribution

Paper: "Extending SAM2 for Universal Image Segmentation with Language Prompts"
Fangxun Shu*, Yiwen Ye*, Jianhua Han, Jiwen Yu, Qize Yang, Xiao-Ping Zhang, Hang Xu, Bei Yu, Xiaodan Liang.
NeurIPS 2025 Spotlight · arXiv 2507.05427

Original code: GinnyXiao/OpenWorldSAM — Apache-2.0 License

Checkpoint: ADE20K instance segmentation (openworld_sam_ade20k.pt) from the official Google Drive release

Usage

Via FiftyOne Model Zoo

python

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart", max_samples=5)
model = foz.load_zoo_model(
    "openworld-sam-ade20k-torch",
    class_names=["person", "car", "chair", "table", "sky", "tree"],
)
dataset.apply_model(model, label_field="owsam_pred")
session = fo.launch_app(dataset)

Standalone (`trust_remote_code`)

python

import torch
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "neerajaabhyankar/openworld-sam", trust_remote_code=True
)
model.eval()

import numpy as np
arr = np.array(your_pil_image)  # HWC uint8 RGB

batched_inputs = [{
    "image":             model.preprocess_image(arr),
    "evf_image":         model.preprocess_image_beit3(arr),
    "height":            arr.shape[0],
    "width":             arr.shape[1],
    "prompt":            ["person", "car", "tree"],
    "unique_categories": [0, 1, 2],
}]

with torch.no_grad():
    outputs = model(batched_inputs)

# outputs[0]["instances"]:
#   "masks"     — bool tensor [N, H, W]
#   "scores"    — float tensor [N]
#   "class_ids" — long tensor [N]

Architecture

Component	Detail
Visual backbone	SAM2 Hiera-Large (frozen, 224M params)
Multimodal encoder	BEiT-3 Large (frozen, 675M params)
Trainable params	~4.5M — projection MLP + positional tokens + 3-layer cross-attention
Total params	~902M
Vocabulary	ADE20K-150 classes (default); any text at inference
SAM2 input	1024×1024, normalised with ImageNet pixel stats
BEiT-3 input	224×224, normalised to mean=0.5 std=0.5

Notes

Loading this checkpoint via from_pretrained prints a LOAD REPORT with a few MISSING/UNEXPECTED keys. Both are understood and safe to ignore for the single-image zero-shot segmentation path documented above:

evf_sam2.text_hidden_fcs.* — MISSING (dead duplicate module, harmless). Both EvfSam2Model.__init__ (model/evf_sam2.py) and OpenWorldSAMModel.__init__ (modeling_openworld_sam.py) independently construct their own text_hidden_fcs projection ModuleList. The checkpoint only ever stored one copy, at the top-level text_hidden_fcs.* key — that's the one OpenWorldSAMModel actually uses in forward() (self.text_hidden_fcs[0](feat)) and it loads correctly. The nested evf_sam2.text_hidden_fcs copy is never referenced anywhere in OpenWorldSAMModel.forward(), so it gets randomly initialized and simply sits unused. No effect on inference output. (Could be cleaned up by removing the unused construction in EvfSam2Model.__init__, but there's no correctness reason to.)
memory_encoder.fuser.layers.{0,1} — gamma/weight name mismatch (real bug, but on an unused code path). In model/segment_anything_2/sam2/modeling/memory_encoder.py, CXBlock has a comment claiming the layer-scale parameter was renamed: # modified by ZhangYx from self.gamma to self.weight. Due to (facebookresearch/segment-anything-2#85) — but the code still declares self.gamma. The checkpoint was produced by the actual ZhangYx fork, which did apply the rename, so its keys are ...weight. Since the bundled code never got the rename, gamma and weight don't match: the checkpoint's weight values show as UNEXPECTED, and the model's gamma parameters show as MISSING (randomly initialized instead of loaded). In practice this doesn't affect the model as used here: memory_encoder (and its fuser) is only invoked from SAM2's video mask-propagation path (self.memory_encoder(...) in sam2_base.py, called during track-step), which OpenWorldSAMModel.forward() never exercises — it only calls visual_model.forward_image() + _prepare_backbone_features() for single-image inference. This would only matter if a future integration adds video/memory-based mask propagation on top of this checkpoint; at that point, fix it by renaming self.gamma → self.weight in CXBlock to match the checkpoint.

Requirements

torch torchvision transformers safetensors timm einops

No detectron2 required — this mirror is self-contained.

License

Apache-2.0 (same as original GinnyXiao/OpenWorldSAM)

Voxel51/openworld-sam

OpenWorldSAM — Zero-Shot Universal Image Segmentation