---
license: mit
tags:
- kaggle
- image-retrieval
- re-identification
- jaguar
- eva02
- dinov2
- arcface
---

# Jaguar Re-Identification - Model Artifacts

Kaggle "[Jaguar Re-Identification Challenge](https://www.kaggle.com/competitions/jaguar-re-id)" コンペティションの学習済みモデル重み・サブミッション・設定ファイルのバックアップ。

## Best Score

**Public LB 0.948** (exp004 + exp005 weighted ensemble, w=0.7/0.3)

## Contents

### Model Weights (`weights/`)

| Experiment | Description | LB Score | File | Size |
|------------|-------------|----------|------|------|
| exp004/000 | EVA-02-L-448 + GeM + ArcFace + Jaccard Rerank + Optimal Blend (10ep) | **0.946** | `weights/exp004_0938_optimal_blending/000/best_model.pth` | ~1.2GB |
| exp005/000 | Pseudo-Labeling fine-tune on exp004 (5ep) | 0.937 | `weights/exp005_pseudo_labeling/000/best_model_finetuned.pth` | ~1.2GB |
| exp006/001 | DINOv2 ViT-L-518 + GeM + ArcFace (20ep) | 0.925 | `weights/exp006_dinov2/001/best_model.pth` | ~1.2GB |

### Submissions (`submissions/`)

各実験の推論結果 CSV (baseline, TTA, rerank, optimal_blend)。

### Configs (`configs/`)

各実験の Hydra 設定ファイル (config.yaml + exp/*.yaml)。

### Docs & Logs (`docs/`, `logs/`)

- `docs/experiments.md`: 全実験結果・知見集約
- `docs/TODO.md`: タスク管理
- `docs/KAGGLE_DIRECTION.md`: コンペ固有ワークフロー
- `logs/Log_*.md`: 日別開発ログ

## Experiment Summary

| Experiment | Backbone | Epochs | Best Loss | Accuracy | LB |
|------------|----------|--------|-----------|----------|----|
| exp002/000 | MegaDescriptor-B-224 | 25 | - | - | 0.781 |
| exp003/000 | EVA-02-L-448 | 10 | - | - | 0.921 |
| exp003/001 | EVA-02-L-448 | 20 | 0.3035 | 96.78% | 0.913 |
| **exp004/000** | **EVA-02-L-448** | **10** | **0.1355** | **98.47%** | **0.946** |
| exp004/001 | EVA-02-L-448 | 20 | 0.0481 | 99.42% | 0.945 |
| exp005/000 | EVA-02-L-448 (PL) | 5 | 0.0731 | 99.20% | 0.937 |
| exp006/000 | DINOv2-L-518 | 10 | 0.3982 | 95.14% | 0.897 |
| exp006/001 | DINOv2-L-518 | 20 | 0.0827 | 98.94% | 0.925 |
| exp007/000 | EVA-02-L-448 (LwLR+W+EMA) | 10 | 4.4556 | 48.94% | - |
| exp007/001 | EVA-02-L-448 (LwLR0.95+W+EMA) | 10 | 0.3466 | 95.56% | - |
| exp007/002 | EVA-02-L-448 (W+EMA) | 10 | 0.1904 | 97.83% | - |
| **Ensemble** | **exp004+exp005 (w0.7/0.3)** | - | - | - | **0.948** |

## Key Insights

- **EVA-02 Large 448px** が最も効果的なバックボーン (MegaDescriptor-B-224 比 LB +0.140)
- **Jaccard Re-ranking + Optimal Blending** (20% raw + 80% reranked) が後処理として有効
- **20ep 延長学習は微小改善〜悪化**: exp003は悪化 (0.921→0.913)、exp004はほぼ同等 (0.946→0.945)
- **Layer-wise LR Decay は EVA-02 Large に有害**: decay_rate=0.95 でも全層同一LR に劣る
- **Warmup + EMA は微小劣化**: 10ep では warmup の序盤学習遅延を回収しきれない

## Reproduction

### Code

```bash
git clone git@github.com:nawta/Jaguar_Re_Identification.git
cd Jaguar_Re_Identification
git checkout lb-0.948
```

### Environment

```bash
# Python environment
uv sync

# or Docker
make build && make bash
```

### Data

Competition data is NOT included. Download from Kaggle:

```bash
kaggle competitions download -c jaguar-re-id
unzip jaguar-re-id.zip -d input/
```

### Training

```bash
# Best single model (exp004)
uv run python -m experiments.exp004_0938_optimal_blending.run exp=000

# Pseudo-labeling (exp005, requires exp004 weights)
uv run python -m experiments.exp005_pseudo_labeling.run exp=000

# DINOv2 (exp006)
uv run python -m experiments.exp006_dinov2.run exp=000
```

### Ensemble (LB 0.948)

exp004 と exp005 の submission.csv を weighted average (w=0.7/0.3) で結合:

```python
import pandas as pd
df004 = pd.read_csv("output/experiments/exp004_0938_optimal_blending/000/submission.csv")
df005 = pd.read_csv("output/experiments/exp005_pseudo_labeling/000/submission.csv")
df_ens = df004.copy()
df_ens["score"] = 0.7 * df004["score"] + 0.3 * df005["score"]
df_ens.to_csv("submission_ensemble.csv", index=False)
```

## Not Included

- **Competition input data** (`input/`, ~17GB): Re-download via `kaggle competitions download -c jaguar-re-id`
- **Inferior model weights**: exp002 (LB 0.781), exp003 (LB 0.921), exp007 (exp004 以下)
- **Python virtual environment** (`.venv/`): Recreate via `uv sync`
- **wandb logs**: Synced to [wandb.ai/nawta1998/jaguar-re-identification](https://wandb.ai/nawta1998/jaguar-re-identification)