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README.md

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-license: apache-2.0
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+# Model Card for Memories-S0
+
+**Memories-S0** is a highly efficient, 3-billion-parameter video understanding model designed specifically for the security and surveillance domain. It leverages synthetic data generation (via Veo 3) and extreme optimization strategies to achieve state-of-the-art performance on edge devices.
+
+## Model Details
+
+* **Model Name:** Memories-S0
+* **Organization:** Memories.ai Research
+* **Model Architecture:** 3B Parameter VideoLLM
+* **Release Date:** Jan 2026
+* **License:** Apache 2.0
+* **Paper:** [Memories-SO: An Efficient and Accurate Framework for Security Video Understanding](https://memories.ai/research/Camera)
+* **Code Repository:** [https://github.com/Memories-ai-labs/memories-s0](https://github.com/Memories-ai-labs/memories-s0)
+
+### Model Description
+
+Memories-S0 is designed to address two key challenges in security video understanding: data scarcity and deployment efficiency on resource-constrained devices.
+
+* **Data Innovation:** The model is pre-trained on a massive, diverse set of synthetic surveillance videos generated by advanced video generation models (like Veo 3). This allows for pixel-perfect annotations and covers diverse scenarios (e.g., dimly lit hallways, unattended packages).
+* **Extreme Efficiency:** It utilizes an innovative input token compression algorithm that dynamically prunes redundant background tokens, focusing computation on foreground objects and motion. This allows the 3B model to run efficiently on mobile/edge hardware.
+* **Post-Training:** The model employs a unique post-training strategy using Reinforcement Learning (RL) and event-based temporal shuffling to enhance sequential understanding without expensive full fine-tuning.
+
+## Intended Use
+
+### Primary Use Cases
+
+* **Security & Surveillance:** Detecting anomalies, tracking suspicious activities, and monitoring public safety.
+* **Smart Home Monitoring:** Analyzing video feeds for unusual events (e.g., falls, intruders) as benchmarked on SmartHomeBench.
+* **Edge Computing:** Deploying high-performance video analysis directly on cameras or local gateways with limited memory and compute power.
+
+### Out-of-Scope Use Cases
+
+* General open-domain video understanding (e.g., movie classification) may not be optimal as the model is specialized for surveillance angles and events.
+* Biometric identification (Face Recognition) is not the primary design goal; the focus is on action and event understanding.
+
+## Performance (SmartHomeBench)
+
+We evaluated Memories-S0(3B) on the **SmartHomeBench** dataset, a recognized benchmark for smart home video anomaly detection.
+
+Despite having only **3B parameters**, our model achieves an **F1-score of 79.21** using a simple **Zero-shot** prompt, surpassing larger models like VILA-13b and performing competitively against GPT-4o and Claude-3.5-Sonnet (which require complex Chain-of-Thought prompting).
+
+| Model | Params | Prompting Method | Accuracy | Precision | Recall | **F1-score** |
+| --- | --- | --- | --- | --- | --- | --- |
+| **Memories-S0 (Ours)** | **3B** | **Zero-shot** | **71.33** | **73.04** | **86.51** | **79.21** |
+| VILA-13b | 13B | Few-shot CoT | 67.17 | 69.18 | 70.57 | 69.87 |
+| GPT-4o | Closed | Zero-shot | 68.41 | 80.09 | 55.16 | 65.33 |
+| Gemini-1.5-Pro | Closed | Zero-shot | 57.36 | 84.34 | 25.73 | 39.43 |
+
+## Citation
+
+If you use this model or framework in your research, please cite our technical report:
+
+```bibtex
+@techreport{memories_s0_2025,
+  title       = {{Memories-S0}: An Efficient and Accurate Framework for Security Video Understanding},
+  author      = {{Memories.ai Research}},
+  institution = {Memories.ai},
+  year        = {2025},
+  month       = oct,
+  url         = {https://huggingface.co/Memories-ai/security_model},
+  note        = {Accessed: 2025-11-20}
+}
+
+```