Align HF README workflow with GitHub instructions

README.md

@@ -33,7 +33,7 @@ PerceptionComp is a benchmark for complex perception-centric video reasoning. It
 
 ### Dataset Description
 
-PerceptionComp contains 1,114 manually annotated five-choice questions associated with 273 videos. The benchmark covers seven categories: outdoor tour, shopping, sport, variety show, home tour, game, and movie.
+PerceptionComp contains 1,114 manually annotated five-choice questions associated with 273 referenced video IDs. The benchmark covers seven categories: outdoor tour, shopping, sport, variety show, home tour, game, and movie.
 
 This Hugging Face dataset repository is intended to host the benchmark videos together with a viewer-friendly annotation file, `questions.json`, for Dataset Preview and Data Studio. The canonical annotation source, evaluation code, and model integration examples are maintained in the official GitHub repository:
 
@@ -68,6 +68,71 @@ PerceptionComp is not intended for:
 - surveillance, identity inference, or sensitive attribute prediction
 - modifying the benchmark protocol and reporting those results as directly comparable official scores
 
+## Evaluation Workflow
+
+The Hugging Face repository hosts the benchmark videos and the viewer-friendly test annotations. The evaluation code lives in the GitHub repository and follows this workflow:
+
+### Step 1. Clone the Repository
+
+```bash
+git clone https://github.com/hrinnnn/PerceptionComp.git
+cd PerceptionComp
+```
+
+### Step 2. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 3. Download the Benchmark Videos
+
+```bash
+python3 scripts/download_data.py --repo-id hrinnnn/PerceptionComp
+```
+
+If the Hugging Face dataset requires authentication:
+
+```bash
+python3 scripts/download_data.py \
+  --repo-id hrinnnn/PerceptionComp \
+  --hf-token YOUR_HF_TOKEN
+```
+
+The download helper fetches video files from the Hugging Face `data/` directory, flattens them into `benchmark/videos/`, and validates the required `video_id` set against `benchmark/annotations/1-1114.json`.
+
+### Step 4. Run Evaluation
+
+OpenAI-compatible API example:
+
+```bash
+python3 evaluate/evaluate.py \
+  --model YOUR_MODEL_NAME \
+  --provider api \
+  --api-key YOUR_API_KEY \
+  --base-url YOUR_BASE_URL \
+  --video-dir benchmark/videos
+```
+
+Gemini example:
+
+```bash
+python3 evaluate/evaluate.py \
+  --model YOUR_GEMINI_MODEL_NAME \
+  --provider gemini \
+  --api-key YOUR_GEMINI_API_KEY \
+  --video-dir benchmark/videos
+```
+
+### Step 5. Check the Outputs
+
+Evaluation outputs are written to:
+
+```text
+evaluate/results/Results-<model>.json
+evaluate/results/Results-<model>.csv
+```
+
 ## Dataset Structure
 
 ### Data Instances
@@ -94,12 +159,17 @@ Core fields in each annotation item:
 
 ### Data Files
 
-This upload bundle contains:
+This Hugging Face dataset repository contains:
 
 - `questions.json`: root-level annotation file used by Hugging Face Dataset Preview and Data Studio
+- `data/<video_id>.<ext>`: benchmark video files downloaded by the official helper script
 - `README.md`: Hugging Face dataset card
 - `LICENSE`: custom research-use terms for the benchmark materials
 
+The canonical annotation file used by the evaluator remains:
+
+- `benchmark/annotations/1-1114.json` in the GitHub repository
+
 The official evaluation code prepares videos into the following local layout:
 
 ```text
@@ -112,14 +182,16 @@ Use the official download script from the GitHub repository:
 git clone https://github.com/hrinnnn/PerceptionComp.git
 cd PerceptionComp
 pip install -r requirements.txt
-python scripts/download_data.py --repo-id hrinnnn/PerceptionComp
+python3 scripts/download_data.py --repo-id hrinnnn/PerceptionComp
 ```
 
+If your environment provides `python` instead of `python3`, use that alias consistently for the commands below.
+
 ### Data Splits
 
 The current public release uses one official evaluation split:
 
-- `test`: 1,114 multiple-choice questions over 273 videos, exposed through `questions.json`
+- `test`: 1,114 multiple-choice questions over 273 referenced video IDs, exposed through `questions.json`
 
 ## Dataset Creation
 
@@ -198,8 +270,8 @@ Example evaluation workflow:
 git clone https://github.com/hrinnnn/PerceptionComp.git
 cd PerceptionComp
 pip install -r requirements.txt
-python scripts/download_data.py --repo-id hrinnnn/PerceptionComp
-python evaluate/evaluate.py \
+python3 scripts/download_data.py --repo-id hrinnnn/PerceptionComp
+python3 evaluate/evaluate.py \
   --model YOUR_MODEL_NAME \
   --provider api \
   --api-key YOUR_API_KEY \