---
license: other
license_name: fair-research-license
license_link: https://huggingface.co/facebook/EUPE-ViT-T/blob/main/LICENSE
base_model: facebook/EUPE-ViT-T
tags:
  - multi-task-perception
  - computer-vision
  - image-classification
  - semantic-segmentation
  - depth-estimation
  - object-detection
  - vision-transformer
  - edge
library_name: pytorch
datasets:
  - imagenet-1k
  - scene_parse_150
  - sayakpaul/nyu_depth_v2
  - detection-datasets/coco
pipeline_tag: image-classification
---

# Argus-Edge

Multi-task perception on a frozen EUPE-ViT-T backbone. Classification, semantic segmentation, metric depth, object detection, and dense correspondence from a single 5.5-million-parameter encoder.

## Architecture

```
Image → EUPE-ViT-T (frozen, 5.5M) → shared features
                                     │
              ┌──────────────────────┼──────────────────────┬──────────────────┐
              ▼                      ▼                      ▼                  ▼
        Classification          Segmentation              Depth            Detection
        Linear(192, 1K)         DPT (dec_dim=128)     DPT (dec_dim=128)  Split-tower (192-D)
        0.19 M params            3.27 M params           3.28 M params       2.88 M params
```

Plus correspondence via dense cosine matching on patch tokens (0 params).

| Component | Params |
|---|---:|
| EUPE-ViT-T backbone (frozen) | 5.50 M |
| Classifier head | 0.19 M |
| Segmentation head | 3.27 M |
| Depth head | 3.28 M |
| Detection head | 2.88 M |
| **Total** | **15.12 M** |

## Results

| Task | Dataset | Metric | Value |
|---|---|---|---:|
| Classification | ImageNet-1k val (50K images) | top-1 / top-5 | **60.31 % / 85.49 %** |
| Segmentation | ADE20K val (2000 images) | mIoU | **0.365** |
| Depth | NYU Depth V2 val (5058 images) | RMSE | **0.625** |
| Detection | COCO val2017 (5000 images) | mAP@[.5:.95] / mAP@.5 | **26.7 / 47.3** |

## Training recipes

| Head | Recipe |
|---|---|
| Classification | Cached 192-D CLS tokens from 1.28 M ImageNet-1k train images, L2-normalized, `Linear(192, 1000)`, AdamW lr 1e-3, 30-epoch cosine |
| Segmentation | ADE20K 20K train at 512 px, DPT decoder over 4 hooked blocks (2, 5, 8, 11), `dec_dim=128`, cross-entropy, AdamW lr 1e-4, cosine |
| Depth | NYU Depth V2 32K train at 416 px, DPT decoder over the same 4 hooked blocks, 256-bin log-depth head, SILog loss on expected depth, AdamW lr 1e-4, cosine, reflection-padded 3×3 convs |

Paper ([arXiv:2603.22387](https://arxiv.org/abs/2603.22387)) reports EUPE-ViT-T IN1k-kNN at 66.3 %, a rough ceiling for a linear probe on these features.

## Files

```
cls_head.safetensors          Linear(192, 1000) classifier
seg_head.safetensors          DPT decoder, 150-class segmentation at 512 px
depth_head.safetensors        DPT decoder, 256 log-depth bins at 416 px
det_head.safetensors          Split-tower detection head, 80-class FCOS at 768 px
argus_edge.py                 ArgusEdge class
cls_val_eval.json             classifier val eval
```

## Usage

```python
from argus_edge import ArgusEdge

model = ArgusEdge.from_pretrained('phanerozoic/argus-edge').cuda().eval()

model.classify('image.jpg', top_k=5)
# {'label': 'tabby', 'score': 0.62, 'top5': [...]}

model.segment('street.jpg')              # (512, 512) int array of ADE20K class ids
model.depth('room.jpg')                  # (416, 416) float array, metric depth in meters
model.detect('photo.jpg', score_thresh=0.3)  # [{'box': [x1,y1,x2,y2], 'score': 0.78, 'class_id': 17}, ...]
model.correspond('a.jpg', 'b.jpg')       # dense patch-token matches between two images

out = model.perceive('a.jpg', image_b='b.jpg')
# keys: classification, segmentation, depth, detection, correspondence
```

Requires `argus.py` from [phanerozoic/argus](https://huggingface.co/phanerozoic/argus) on `sys.path` for `DinoVisionTransformer` and `SplitTowerHead`.

## Source backbone

EUPE-ViT-T from Meta FAIR ([arXiv:2603.22387](https://arxiv.org/abs/2603.22387), Zhu et al., March 2026). Three-stage distillation from PEcore-G + PElang-G + DINOv3-H+ via a 1.9 B proxy teacher. License: FAIR Research License (non-commercial).