Update README.md

README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+size_categories:
+- 1K<n<10K
+---
+
+# CVPR-NTIRE Video Saliency Prediction Challenge 2026
+
+[![Page](https://img.shields.io/badge/Challenge-Page-blue)](https://www.codabench.org/competitions/12842/)
+
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/623a3b20fa4890c51b04cba7/nmWH-xSewvOvlicuMPejp.mp4"></video>
+
+## Dataset
+
+We provide a novel audio-visual mouse saliency dataset with the following key-features:
+* Diverse content: movie, sports, live, vertical videos, etc.;
+* Large scale: **2000** videos with mean **18s** duration;
+* High resolution: all streams are **FullHD**;
+* **Audio** track saved and played to observers;
+* Mouse fixations from **>5000** observers (**>70** per video);
+* License: **CC-BY**;
+
+File structure:
+1) `Videos.zip` — 2000 (1200 Train + 800 Test) .mp4 video (kindly reminder: videos contain an audio stream and users watched the video with the sound turned ON!) 
+
+2) `TrainTestSplit.json` — in this JSON we provide Train/Public Test/Private Test split of all videos 
+
+3) `SaliencyTrain.zip/SaliencyTest.zip` — almost losslessly (crf 0, 10bit, min-max normalized) compressed continuous saliency maps videos for Train/Test subset 
+
+4) `FixationsTrain.zip/FixationsTest.zip` — contains the following files for Train/Test subset: 
+
+* `.../video_name/fixations.json` — per-frame fixations coordinates, from which saliency maps were obtained, this JSON will be used for metrics calculation
+
+* `.../video_name/fixations_maps/` — binary fixation maps in '.png' format (since some fixations could share the same pixel, this is a lossy representation and is NOT used either in calculating metrics or generating Gaussians, however, we provide them for visualization and frames count checks)
+
+5) `VideoInfo.json` — meta information about each video (e.g. license)
+
+## Evaluation
+
+### Environment Setup
+
+```
+conda create -n saliency python=3.8.16
+conda activate saliency
+pip install numpy==1.24.2 opencv-python==4.7.0.72 tqdm==4.65.0
+conda install ffmpeg=4.4.2 -c conda-forge
+```
+### Run Evaluation
+Archives with videos were accepted from challenge participants as submissions and scored using the same pipeline as in `bench.py`.
+
+Usage example:
+
+1) Check that your predictions match the structure and names of the [baseline CenterPrior submission](https://drive.google.com/file/d/1rPgMdb4L79OD2vvpDQyqWZIDox78rmxG/view)
+2) Install `pip install -r requirments.txt`, `conda install ffmpeg`
+3) Download and extract `SaliencyTest.zip`,  `FixationsTest.zip`, and `TrainTestSplit.json` files from the dataset page
+4) Run `python bench.py` with flags:
+* `--model_video_predictions ./SampleSubmission-CenterPrior` — folder with predicted saliency videos
+* `--model_extracted_frames ./SampleSubmission-CenterPrior-Frames` — folder to store prediction frames (should not exist at launch time), requires ~170 GB of free space
+* `--gt_video_predictions ./SaliencyTest/Test` — folder from dataset page with gt saliency videos
+* `--gt_extracted_frames ./SaliencyTest-Frames` — folder to store ground-truth frames (should not exist at launch time), requires ~170 GB of free space
+* `--gt_fixations_path ./FixationsTest/Test` — folder from dataset page with gt saliency fixations
+* `--split_json ./TrainTestSplit.json` — JSON from dataset page with names splitting
+* `--results_json ./results.json` — path to the output results json
+* `--mode public_test` — public_test/private_test subsets
+5) The result you get will be available following `results.json` path
+
+[![Challenges](https://img.shields.io/badge/Challenges-NTIRE%202026-orange)](https://www.cvlai.net/ntire/2026/)
+[![Benchmarks](https://img.shields.io/badge/Benchmarks-VideoProcessing-purple)](https://videoprocessing.ai/benchmarks/)