---
license: apache-2.0
tags:
- image-text-retrieval
- jepa
- contrastive-learning
- video-encoder
- pytorch
pipeline_tag: feature-extraction
---

# scout-eccv-itm-v3

Part of **SCOUT** (Sim-to-real Contrastive Outlier Understanding via Text), an ECCV 2026 workshop
paper on JEPA-based text-based person re-identification for the AI City Challenge Track 4 (Sim2Real).
Code: https://github.com/abtraore/SCOUT-ECCV

ScoutITM-v3: a single-stream cross-encoder (ExPLoRA V-JEPA2 + LoRA-adapted EmbeddingGemma token states, bidirectional fusion transformer + ITC/ITM heads) used as a reranker in the paper's headline board system.

- **Val mAP@10**: 0.8858
- **Val Hit@10**: 0.9854
- **Trainable / total parameters**: 120.3M / 656.1M

Blend of ITC-retrieval order and ITM match logit (a=0.5); pure ITM re-ranking alone is worse than ITC alone, always use the blend. See system/README.md.

## What's in this repo

Only the parameters SCOUT actually trained: a `model.safetensors` (trainable predictor weights, and
for ExPLoRA/LoRA variants, the adapted encoder deltas) plus a `config.json` recording the exact recipe.
Frozen backbone weights (V-JEPA2 from Meta, the text encoder from its own source) are **not** included
here; they're downloaded from their own public sources at load time.

## Usage

```python
from scripts.load_from_hf import load_from_hf

model = load_from_hf("Abdrah/scout-eccv-itm-v3").eval()
# model.encode_image(pixel_values)  # [B, 3, 384, 384] in [0, 1] -> [B, out_dim] L2-normalized
# model.encode_text(["a caption"])  # -> [B, out_dim] L2-normalized
```

Or drop it straight into the repo's own evaluation script:

```bash
uv run python scripts/local_eval.py --hf-repo Abdrah/scout-eccv-itm-v3 --pab-root /path/to/PAB --query-source val
```

## License

Apache 2.0 (see the source repo's LICENSE/NOTICE). This checkpoint does not redistribute any frozen
backbone weights, which carry their own upstream licenses.