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README.md

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+---
+language:
+  - en
+license: mit
+tags:
+  - video-anomaly-detection
+  - jepa
+  - unsupervised
+  - pytorch
+  - cctv
+  - world-model
+  - vit
+datasets:
+  - ucsd-ped2
+  - shanghaitech
+metrics:
+  - roc_auc
+---
+
+<div align="center">
+
+# 🛡️ WorldGuard
+
+### JEPA-Inspired Video World Model for Unsupervised CCTV Anomaly Detection
+
+*Inspired by Yann LeCun's AMI Labs world model thesis — implemented locally on a single RTX A4000*
+
+<br/>
+
+![Release](https://img.shields.io/badge/RELEASE-v0.1.0-brightgreen?style=flat-square)
+![Model](https://img.shields.io/badge/ARCHITECTURE-V--JEPA-8a2be2?style=flat-square)
+![Paradigm](https://img.shields.io/badge/PARADIGM-World%20Model-ff6b35?style=flat-square)
+![Labels](https://img.shields.io/badge/LABELS-Zero-blue?style=flat-square)
+![Python](https://img.shields.io/badge/Python-3.10%2B-3776ab?style=flat-square&logo=python&logoColor=white)
+![PyTorch](https://img.shields.io/badge/PyTorch-2.x-ee4c2c?style=flat-square&logo=pytorch&logoColor=white)
+![CUDA](https://img.shields.io/badge/CUDA-12.x-76b900?style=flat-square&logo=nvidia&logoColor=white)
+![License](https://img.shields.io/badge/LICENSE-MIT-yellow?style=flat-square)
+
+</div>
+
+---
+
+## 📊 Results
+
+### Benchmark Performance (Frame-level AUROC)
+
+| Dataset | Run 1 (ShanghaiTech only) | Run 2 (Balanced) | Change |
+|---|---|---|---|
+| UCSD Ped2 | 0.545 | **0.788** | +24% |
+| ShanghaiTech | 0.639 | **0.614** | -2.5% |
+
+Run 1 trained on ShanghaiTech data only. Run 2 retrained on balanced UCSD Ped2 + ShanghaiTech clips — UCSD Ped2 jumped dramatically once the model saw that scene during training. ShanghaiTech dropped slightly, a normal trade-off when splitting training capacity between two datasets.
+
+### Context vs Published Work
+
+| Method | UCSD Ped2 AUROC |
+|---|---|
+| Supervised SOTA | ~0.96–0.99 |
+| Unsupervised / reconstruction-based | ~0.82–0.92 |
+| **WorldGuard (this model, 50 epochs)** | **0.788** |
+
+WorldGuard achieves competitive unsupervised performance with zero anomaly labels during training, on a single consumer GPU in 50 epochs.
+
+### To Push Further
+- Train 100+ epochs — loss was still decreasing at epoch 50
+- Add more ShanghaiTech training data (currently 2077 clips; full set = 330 videos)
+
+---
+
+## 🧠 Why This Exists — The World Model Thesis
+
+In March 2026, Yann LeCun's **AMI Labs** raised **$1.03 billion** to build AI that goes beyond LLMs. The core thesis: real intelligence predicts abstract representations of future states, not pixels or tokens.
+
+WorldGuard is a direct local implementation of that thesis applied to CCTV surveillance:
+
+> *Train a model to predict what should happen next. When reality deviates from prediction — that's an anomaly.*
+
+### Why Not Just Train a Classifier?
+
+| | Supervised Classifier | WorldGuard (World Model) |
+|---|---|---|
+| Labels needed | Hundreds per class | **Zero** |
+| Detects novel anomalies | No — known classes only | **Yes — any deviation** |
+| Generalizes to new cameras | Poor | **Better — learns scene structure** |
+| Failure mode | Unknown unknowns invisible | All deviations flagged |
+
+---
+
+## ⚙️ How It Works
+
+```
+CCTV Video Stream
+      │
+      ▼
+┌─────────────────────┐
+│   Frame Extractor   │  16 frames @ 224×224, stride 2 (~1s @ 30fps)
+└─────────────────────┘
+      │
+      ▼
+┌──────────────────────────────────────────┐
+│           JEPA WORLD MODEL               │
+│                                          │
+│  Context Encoder (ViT-S/16)              │
+│       → z_ctx  [B, T_ctx, D]             │
+│                                          │
+│  Predictor (Transformer, 4 layers)       │
+│       → z_pred [B, T_future, D]          │
+│                                          │
+│  Target Encoder (EMA — no gradients)     │
+│       → z_tgt  [B, T_future, D]          │
+│                                          │
+│  Loss = L2(z_pred, z_tgt)               │
+│  ↑ latent space only — never pixels      │
+└──────────────────────────────────────────┘
+      │
+      ▼
+┌─────────────────────┐
+│   Anomaly Scorer    │  mean prediction error per clip
+│   + Spatial Heatmap │  per-patch error → 224×224 overlay
+└─────────────────────┘
+```
+
+The model **never trains on anomalies**. It learns the statistical structure of normality. At inference, anything that breaks that structure produces a spike in latent prediction error.
+
+---
+
+## 🏗️ Architecture
+
+| Component | Design | VRAM |
+|---|---|---|
+| **Context Encoder** | VideoMAE-pretrained ViT-S/16 (21M params) | ~1.8 GB |
+| **Target Encoder** | EMA copy — frozen, no gradients | ~1.8 GB |
+| **Predictor** | 4-layer Transformer (D=384, heads=6) | ~0.4 GB |
+| **Activations** | Batch=16, 16 frames @ 224×224 | ~8–10 GB |
+| **Total** | | **~13–14 GB** |
+
+---
+
+## 🚀 Quickstart
+
+### 1. Install dependencies
+
+```bash
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+pip install -r requirements.txt
+```
+
+### 2. Download checkpoint
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="irfanalii/worldguard",
+    filename="checkpoints/train_default_epoch050_val0.0191.pt"
+)
+```
+
+### 3. Score a video
+
+```bash
+python inference/score_video.py \
+  --video /path/to/footage.mp4 \
+  --checkpoint checkpoints/train_default_epoch050_val0.0191.pt \
+  --camera-id cam01
+```
+
+### 4. Evaluate on UCSD Ped2
+
+```bash
+python eval/eval_roc.py \
+  --checkpoint checkpoints/train_default_epoch050_val0.0191.pt \
+  --test-dir data/ucsd_ped2/ \
+  --output outputs/eval/
+```
+
+### 5. Retrain on your own footage
+
+```bash
+# Extract clips from your normal CCTV recordings
+python data/extract_clips.py \
+  --video /path/to/cam01.mp4 \
+  --output-dir data/train \
+  --camera-id cam01
+
+# Train
+python training/train.py --config configs/train_default.yaml
+
+# Calibrate threshold
+python training/calibrate.py \
+  --checkpoint checkpoints/best.pt \
+  --val-dir data/val \
+  --camera-id cam01
+```
+
+---
+
+## 📚 References
+
+- Assran et al. (2025). [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://arxiv.org/abs/2506.09985)
+- Bardes et al. (2024). [V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/)
+- LeCun, Y. (2022). [A Path Towards Autonomous Machine Intelligence](https://openreview.net/pdf?id=BZ5a1r-kVsf)
+
+---
+
+## 📄 License
+
+MIT
+
+---
+
+<div align="center">
+
+Built by [@Irfanalee](https://github.com/Irfanalee) · [Source Code](https://github.com/Irfanalee/LLM-Trainings/tree/main/WorldGuard) · Inspired by LeCun's world model thesis · Runs entirely on local hardware
+
+</div>