Improve model card: Update license, image paths, and HF link

README.md

@@ -1,31 +1,32 @@
 ---
-license: cc-by-4.0
 language:
 - en
+license: cc-by-nc-4.0
 metrics:
 - roc_auc
 - matthews_correlation
 pipeline_tag: video-classification
 ---
+
 # Simplifying Traffic Anomaly Detection with Video Foundation Models
 
 Svetlana Orlova, Tommie Kerssies, Brunó B. Englert, Gijs Dubbelman \
 Eindhoven University of Technology
 
 [![arXiv](https://img.shields.io/badge/cs.CV-2507.09338-b31b1b?logo=arxiv&logoColor=red)](https://arxiv.org/abs/2507.09338)
-[![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Models-SimpleTAD-blue)](https://huggingface.co/tue-mps/simple-tad/tree/main/models) 
+[![Hugging Face Models](https://img.shields.io/badge/%F0%9F%A4%97%20Models-SimpleTAD-blue)](https://huggingface.co/tue-mps/simple-tad) 
 [![Code](https://img.shields.io/badge/Code-simple--tad-black?logo=github)](https://github.com/tue-mps/simple-tad)
 
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@@ -33,11 +34,11 @@ Eindhoven University of Technology
 
 Recent methods for ego-centric Traffic Anomaly Detection (TAD) often rely on complex multi-stage or multi-representation fusion architectures, yet it remains unclear whether such complexity is necessary. Recent findings in visual perception suggest that foundation models, enabled by advanced pre-training, allow simple yet flexible architectures to outperform specialized designs. Therefore, in this work, we investigate an architecturally simple encoder-only approach using plain Video Vision Transformers (Video ViTs) and study how pre-training enables strong TAD performance. We find that: (i) advanced pre-training enables simple encoder-only models to match or even surpass the performance of specialized state-of-the-art TAD methods, while also being significantly more efficient; (ii) although weakly- and fully-supervised pre-training are advantageous on standard benchmarks, we find them less effective for TAD. Instead, self-supervised Masked Video Modeling (MVM) provides the strongest signal; and (iii) Domain-Adaptive Pre-Training (DAPT) on unlabeled driving videos further improves downstream performance, without requiring anomalous examples. Our findings highlight the importance of pre-training and show that effective, efficient, and scalable TAD models can be built with minimal architectural complexity.
 
-![Simple_Main](illustrations/main.png)
+![Simple_Main](figs/main.png)
 
 ### ✨ DoTA and DADA-2000 results
 
-![Simple_Results](illustrations/results.png)
+![Simple_Results](figs/results.png)
 
 Video ViT-based encoder-only models set a new state of the art
 on both datasets, while being significantly more efficient than top-performing specialized methods. FPS measured using NVIDIA A100
@@ -111,6 +112,14 @@ We used fragments of original implementations of
 [InternVideo2](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/single_modality), 
 and [UMT](https://github.com/OpenGVLab/unmasked_teacher/tree/main/single_modality) to integrate these models with our codebase.
 
+## 🔒 License
+
+The majority of this project is released under the CC-BY-NC 4.0 license as found in the [LICENSE](https://github.com/MCG-NJU/VideoMAE/blob/main/LICENSE) file. 
+Portions of the project are available under separate license terms: 
+[ViViT](https://github.com/google-research/scenic/blob/main/scenic/projects/vivit/README.md), [InternVideo2](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2/single_modality), [SlowFast](https://github.com/facebookresearch/SlowFast) and [pytorch-image-models](https://github.com/rwightman/pytorch-image-models) are licensed under the Apache 2.0 license. 
+[VideoMAE2](https://github.com/OpenGVLab/VideoMAEv2), [SMILE](https://github.com/fmthoker/SMILE), [MGMAE](https://github.com/MCG-NJU/MGMAE), [UMT](https://github.com/OpenGVLab/unmasked_teacher/tree/main/single_modality), and [BEiT](https://github.com/microsoft/unilm/tree/master/beit) are licensed under the MIT license.
+[SIGMA](https://github.com/QUVA-Lab/SIGMA/) is licensed under the BSD 3-Clause Clear license
+
 ## ✏️ Citation
 
 If you think this project is helpful, please feel free to like us ❤️ and cite our paper:
@@ -137,12 +146,12 @@ If you think this project is helpful, please feel free to like us ❤️ and cit
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