Argus
Argus is a multi-task vision model: task heads on a frozen EUPE-ViT-B backbone (Zhu et al., Meta FAIR, arXiv:2603.22387), 103M parameters in total, of which 17M are the heads.
| Task | Head | Output | Trained on | Result |
|---|---|---|---|---|
| Classification | Linear softmax on the L2-normalized CLS token | 1,000 ImageNet classes | ImageNet-1k | 85.53% top-1, 97.69% top-5 (val) |
| Segmentation | BatchNorm and 1×1 conv | 150 ADE20K classes | ADE20K | 52.72 mIoU (val) |
| Depth | DPT decoder on blocks 2, 5, 8 and 11, 256 bins over 0.001–10 m | Metric depth | NYU Depth V2 | RMSE 0.452, abs rel 0.117, δ<1.25 0.852 (654-image Eigen test split, Eigen crop, evaluate.py) |
| Detection | Cofiber pyramid with a CLIP-text-aligned cosine classifier, FCOS decoding | 80 COCO classes | COCO 2017 | 42.64 mAP (val2017) |
| Correspondence | None: cosine matching of patch features | Patch matches | — | 54.35 PCK@0.1 (SPair-71k test) |
The detection head is documented in
phanerozoic/cofiber-detection.
Its class-agnostic AR@100 across 20 RF100-VL domains is 0.289, with the
per-domain results in rf100vl_zero_shot_cross_domain_eval.json. Smaller
variants are argus-lite
(EUPE-ViT-S) and argus-edge
(EUPE-ViT-T).
Usage
from PIL import Image
from transformers import AutoModel
model = AutoModel.from_pretrained("phanerozoic/argus", trust_remote_code=True)
image = Image.open("image.jpg").convert("RGB")
model.classify(image, top_k=5) # [{"class_id", "class_name", "score"}, ...]
model.segment(image) # [H, W] ADE20K class indices
model.depth(image) # [H, W] depth in meters
model.detect(image, score_thresh=0.3) # [{"box", "score", "label", "class_name"}, ...]
model.embed(image) # [768] L2-normalized CLS embedding
model.perceive(image) # classification, segmentation and depth
model.correspond(image, other_image) # {"matches", "scores", "grid"}
Each method also takes a list of images and returns a list of results. Images
are resized to the resolution of the head: 224 for classification and embedding, 512 for
segmentation, 416 for depth and 768 for detection, which letterboxes the
image. Segmentation and depth maps come back at each image's own height and
width, and boxes in its pixel coordinates. segment() and depth() return
per-pixel confidence and depth standard deviation with
return_confidence=True, and model.seg_class_names names the segmentation
indices. The depth head is trained on indoor scenes and predicts at most
10 m.
variant="bf16_backbone" loads the 241 MB model.bf16_backbone.safetensors
in place of the 412 MB model.safetensors, into the same FP32 model.
model.compile() compiles the backbone blocks and the heads with
torch.compile, model.quantize_int8() applies INT8 weight-only
quantization through torchao, and model.export_onnx(out_dir, verify=True)
exports the backbone and the heads to ONNX and checks the graphs against
PyTorch with ONNX Runtime.
evaluate.py scores the classification, segmentation, depth and detection
heads on ImageNet-1k, ADE20K, NYU Depth V2 and COCO:
python evaluate.py imagenet --root imagenet/val
python evaluate.py ade20k --root ADEChallengeData2016
python evaluate.py nyu --root nyu_test
python evaluate.py coco --images val2017 --annotations instances_val2017.json
License
The EUPE-ViT-B weights are released under the
FAIR Noncommercial Research License.
The checkpoint contains them and is released under the same license; see
LICENSE.
Citation
@misc{zhu2026eupe,
title={Efficient Universal Perception Encoder},
author={Zhu, Chenchen and Suri, Saksham and Jose, Cijo and Oquab, Maxime and Szafraniec, Marc and Wen, Wei and Xiong, Yunyang and Labatut, Patrick and Bojanowski, Piotr and Krishnamoorthi, Raghuraman and Chandra, Vikas},
year={2026},
eprint={2603.22387},
archivePrefix={arXiv},
primaryClass={cs.CV}
}