---
license: mit
library_name: pytorch
tags:
  - occlusion-detection
  - image-classification
  - dinov3
  - torchscript
  - rgb
  - thermal
  - multimodal
  - orthographic-projection
  - wildlife
  - uav
inference: false
---

# Occlusion Classifiers (DINOv3 + triplet-reduced head)

Binary **clear vs occluded** classifiers in an **embedding + head** design: a raw DINOv3 ViT-H+
embedding goes in, a **triplet-loss-trained projection** reduces it to a 256-d discriminative
embedding, and a logistic head reads that. Each self-contained TorchScript `.pt` records **both**
the reducer and the classifier.

Nine models are published here — three **projection types** x three **modalities**.

| directory | projection | renders |
|---|---|---|
| `non_geo/` | perspective (non-orthographic) | — |
| `geo_1k/`  | orthographic | 1k resolution |
| `geo_2k/`  | orthographic | 2k resolution |

Each contains `occlusion_rgb.pt`, `occlusion_thermal.pt`, and `occlusion_matched.pt`.

## Inputs and outputs

`forward(x)` takes **raw DINOv3 CLS features** `(N, D)` and returns a tuple:

- `embedding` `(N, 256)`, L2-normalised — the triplet-reduced vector, for re-ID / clustering / viz
- `probs` `(N, 2)` — in `classes` order `["clear", "occluded"]`

`D` depends on the modality (also readable at runtime as `m.mu.shape[0]`):

| modality | `D` | features |
|---|---|---|
| `rgb` | 1280 | RGB CLS |
| `thermal` | 1280 | thermal CLS |
| `matched` | 2560 | `[rgb, thermal]` concatenated, in that order |

```python
import torch
from huggingface_hub import hf_hub_download

path = hf_hub_download("cpraschl/bambi-occlusion-classifiers", "geo_2k/occlusion_matched.pt")
m = torch.jit.load(path)              # no .eval() needed (BatchNorm folded in)

with torch.no_grad():                 # see note below
    emb, probs = m(feats)             # feats: (N, 2560) raw DINOv3 features

label = m.classes[int(probs[0].argmax())]
confidence = float(probs[0].max())
```

**Wrap inference in `torch.no_grad()`.** These models were exported with their parameters still
requiring grad, so calling them outside `no_grad` builds an autograd graph on every call and returns
outputs with `requires_grad=True` — you get a warning on `float(...)` and needless memory growth in a
batch loop. `no_grad` avoids both. (`.eval()` genuinely is unnecessary — BatchNorm is folded in.)

Exported attributes: `m.classes` (`["clear", "occluded"]`), `m.emb_dim` (`256`),
`m.reduction` (`"triplet-loss (batch-hard)"`).

> These models consume **DINOv3 features, not images**. You must run the DINOv3 ViT-H+ backbone
> yourself to produce `feats`. The backbone is separate, unchanged, and **not** redistributed here —
> see the note on its licensing below.

## Architecture

```
standardize(mu, sd)
  -> Linear(D, 512) + BatchNorm (folded)
  -> ReLU
  -> Linear(512, 256)
  -> L2-normalise            <- triplet loss, batch-hard
  -> logistic head
```

BatchNorm is fused into the linear layers, so the exported model is deterministic and needs no
`.eval()` call.

## Training data

Trained on the **[BAMBI UAV dataset](https://github.com/bambi-eco/Dataset)** — 389 paired RGB and
thermal aerial video sequences recorded by dual-sensor nadir UAVs over Austrian forest habitats,
with ~5,100 annotated animal tracks across 12 species classes. The `non_geo` models use the
perspective (as-recorded) frames; `geo_1k` and `geo_2k` use orthographic projections of the same
material at 1k and 2k render resolution.

Two properties of BAMBI shape the models directly:

- **Tracks are the grouping unit.** Evaluation uses GroupKFold *by track* so that frames from one
  animal's sequence never straddle the train/test split — without this, near-duplicate consecutive
  frames would inflate accuracy.
- **RGB and thermal are not temporally synchronized** in BAMBI. The `matched` models consume
  frame-level aligned RGB/thermal pairs, so their `(N, 2560)` input assumes you have already solved
  that alignment; they are not a way to avoid it.

## Evaluation

Held-out **balanced accuracy**, GroupKFold by track (grouping by track prevents frames from the same
sequence appearing in both train and test):

| version | rgb | thermal | matched |
|---|---|---|---|
| `non_geo` | 0.769 | 0.748 | **0.845** |
| `geo_1k`  | 0.774 | 0.739 | 0.812 |
| `geo_2k`  | **0.788** | 0.757 | 0.804 |

`matched` (both modalities fused) is strongest across every projection type. The 2k orthographic
renders help `rgb` (0.774 -> 0.788), mirroring the detectors.

## Limitations

- **Feature-space bound.** Each model is tied to the exact DINOv3 ViT-H+ features it was trained on.
  A different backbone, checkpoint, or preprocessing pipeline will silently degrade accuracy — the
  model has no way to detect the mismatch and will still return confident-looking probabilities.
- **Match the model to the projection.** The three versions are trained on different projections and
  are not interchangeable; running perspective features through `geo_2k/` (or vice versa) is an
  out-of-distribution use.
- **Ceiling.** Best balanced accuracy is 0.845, so these are useful filters, not authoritative
  labels. Do not use them where a false "clear" carries real cost without a human in the loop.
- **`matched` requires both modalities.** It expects genuinely paired, co-registered RGB and thermal
  features; feeding it zeros or an unpaired thermal frame for the missing half is not a supported
  fallback.

## Citation

If you use these models, please also cite the underlying BAMBI dataset — the authors ask that work
building on it cite their CV4Animals workshop paper (Praschl et al., 2026). See
[bambi-eco/Dataset](https://github.com/bambi-eco/Dataset) for the current citation.

## Licensing note on the DINOv3 backbone

These are lightweight heads released here under the **MIT license**. They are, however, only useful
on top of **DINOv3**, which is distributed by Meta under **its own separate license terms** that are
not MIT and carry their own usage restrictions. The MIT license here covers *these heads only* — it
grants no rights to DINOv3. If you intend to use these models, review the DINOv3 license yourself and
confirm your use case is permitted under it.