Add model card for DeepIntuit

Hi! I'm Niels from the Hugging Face community science team. I noticed that this repository was missing a model card and metadata. I've opened this PR to add a description of the project, links to the paper and code, and the necessary metadata tags. This will help users find and use your model more effectively on the Hub.

README.md

@@ -0,0 +1,61 @@
+---
+pipeline_tag: video-classification
+library_name: transformers
+tags:
+- video-reasoning
+- VLM
+- reinforcement-learning
+---
+---
+
+# DeepIntuit
+
+[DeepIntuit](https://bwgzk-keke.github.io/DeepIntuit/) is a progressive framework for **open-instance video classification** that evolves models from simple feature imitation to intrinsic reasoning.
+
+- **Paper:** [From Imitation to Intuition: Intrinsic Reasoning for Open-Instance Video Classification](https://huggingface.co/papers/2603.10300)
+- **Repository:** [BWGZK-keke/DeepIntuit](https://github.com/BWGZK-keke/DeepIntuit)
+- **Project Page:** [https://bwgzk-keke.github.io/DeepIntuit/](https://bwgzk-keke.github.io/DeepIntuit/)
+
+## Model Description
+
+DeepIntuit bridges the gap between traditional video encoders and the generalization capabilities of vision-language models (VLMs). Instead of directly predicting labels from visual features, it utilizes a three-stage reasoning pipeline:
+
+1.  **Cold-start supervised alignment:** Initializes reasoning capability using supervised traces generated by a teacher model.
+2.  **Intrinsic reasoning refinement (Stage 1):** Refines the reasoning ability using **Group Relative Policy Optimization (GRPO)** reinforcement learning to enhance coherence.
+3.  **Intuitive calibration (Stage 2):** Trains a classifier on the intrinsic reasoning traces to ensure stable knowledge transfer and accurate classification results.
+
+This approach decouples reasoning generation from final decision-making, significantly improving robustness in scenarios with vast intra-class variations.
+
+## Installation
+
+The repository contains separate environments for each stage. For inference using the final model, set up the stage 2 environment:
+
+```bash
+git clone https://github.com/BWGZK-keke/DeepIntuit.git
+cd DeepIntuit/stage2_model
+pip install -r requirements.txt
+```
+
+## Sample Usage
+
+After setting up the environment, you can run inference using the following command provided in the official repository:
+
+```bash
+cd stage2_model
+python inference.py \
+  --model_path BWGZK/DeepIntuit \
+  --video_path path_to_your_video.mp4
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{zhang2026deepintuit,
+  title={From Imitation to Intuition: Intrinsic Reasoning for Open-Instance Video Classification},
+  author={Zhang, Ke and Zhao, Xiangchen and Tian, Yunjie and Zheng, Jiayu and Patel, Vishal M and Fu, Di},
+  journal={arXiv preprint arXiv:2603.10300},
+  year={2026}
+}
+```