Update README.md

README.md

@@ -7,4 +7,60 @@ configs:
   - split: test
     path: "Test/**"
 license: cc-by-4.0
----
+---
+
+
+# CrowdSAL: Video Saliency Dataset and Benchmark
+
+
+## Dataset
+[![Dataset on HF](https://huggingface.co/datasets/huggingface/badges/resolve/main/dataset-on-hf-md-dark.svg)](https://huggingface.co/datasets/ANDRYHA/CrowdSAL)
+[![Google Drive](https://img.shields.io/badge/Google%20Drive-4285F4?style=for-the-badge&logo=googledrive&logoColor=white)](https://drive.google.com/drive/folders/1daH-14w_vHLc9OuGQ_RU0HgUv_Wc3G0o?usp=sharing)
+
+
+**CrowdSAL** is the largest video saliency dataset with the following key features:
+* Large scale: **5000** videos with mean **18.4s** duration, **2.7M+** frames;
+* Mouse fixations from **>19000** observers (**>75** per video);
+* **Audio** track saved and played to observers;
+* High resolution: all streams are **FullHD**;
+* Diverse content from **YouTube, Shorts, Vimeo**;
+* License: **CC-BY**;
+
+### File Structure
+
+1) `Train/Test` folders — dataset splits, ids 0001-3000 are from Train, 3001-5000 from Test subset;
+  
+2) `Videos` — 5000 mp4 FullHD, 30 FPS videos with audio streams;
+
+3) `Saliency` — 5000 mp4 almost losslessly (crf 0, 10bit, min-max normalized) compressed continuous saliency maps videos;
+
+4) `Fixations` — 5000 json files with per-frame fixation coordinates, from which saliency maps were obtained;
+
+5) `metadata.jsonl` — meta information about each video (e.g. license, source URL, etc.)
+
+
+## Benchmark Evaluation
+
+### Environment Setup
+
+```
+conda create -n saliency python=3.10.19
+conda activate saliency
+pip install numpy==2.2.6 opencv-python-headless==4.12.0.88 tqdm==4.67.1
+conda install ffmpeg=4.4.2 -c conda-forge
+```
+### Run Evaluation
+Usage example:
+
+1) Check that your predictions match the structure and names of the Test dataset subset;
+2) Install all dependencies from Environment Setup;
+3) Download and extract all CrowdSAL files from the dataset page;
+4) Run `python bench.py` with flags:
+* `--model_video_predictions` — folder with predicted saliency videos
+* `--model_extracted_frames` — folder to store prediction frames (should not exist at launch time)
+* `--gt_video_predictions` — folder from dataset page with gt saliency videos
+* `--gt_extracted_frames` — folder to store ground-truth frames (should not exist at launch time)
+* `--gt_fixations_path` — folder from dataset page with gt saliency fixations
+* `--mode` — Train/Test subsets split
+* `--results_json` — path to the output results json
+5) The result you get will be available following `results_json` path.