Update README.md

README.md

@@ -33,6 +33,9 @@ File structure:
 
 5) `VideoInfo.json` — meta information about each video (e.g. license)
 
+6) `SampleSubmission.zip` — example Center Prior submission for the challenge, obtained from fitter mean Gaussian over training saliency maps.
+
+
 ## Evaluation
 
 ### Environment Setup
@@ -48,17 +51,18 @@ Archives with videos were accepted from challenge participants as submissions an
 
 Usage example:
 
-1) Install `pip install -r requirments.txt`, `conda install ffmpeg`
-2) Download and extract `SaliencyTest.zip`,  `FixationsTest.zip`, and `TrainTestSplit.json` files from the dataset page
-3) 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
+1) Check that your predictions match the structure and names of the baseline SampleSubmission.zip submission.
+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` — folder with predicted saliency videos
+* `--model_extracted_frames ./SampleSubmission-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
-4) The result you get will be available following `results.json` path
+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/)