Update README.md

README.md

@@ -48,10 +48,9 @@ Archives with videos were accepted from challenge participants as submissions an
 
 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:
+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
 * `--gt_video_predictions ./SaliencyTest/Test` — folder from dataset page with gt saliency videos
@@ -60,6 +59,6 @@ Usage example:
 * `--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
+4) 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/)