Update docs

Author: afterthat97

README.md

@@ -45,6 +45,7 @@ If you use the challenge dataset in your paper, please consider citing OccNet wi
   - [Introduction](#introduction)
   - [News](#news)
   - [Task Definition](#task-definition)
+    - [Rules for Occupancy and Flow Challenge](#rules-for-occupancy-and-flow-challenge)
   - [Evaluation Metrics](#evaluation-metrics)
     - [Ray-based mIoU](#evaluation-metrics)
     - [AVE for Occupancy Flow](#evaluation-metrics)
@@ -62,6 +63,17 @@ If you use the challenge dataset in your paper, please consider citing OccNet wi
 
 Given images from multiple cameras, the goal is to predict the semantics and flow of each voxel grid in the scene.
 
+### Rules for Occupancy and Flow Challenge
+
+* We allow using annotations provided in the nuScenes dataset. During inference, the input modality of the model should be camera only. 
+* No future frame is allowed during inference.
+* In order to check the compliance, we will ask the participants to provide technical reports to the challenge committee and the participant will be asked to provide a public talk about the method after winning the award.
+* Every submission provides method information. We encourage publishing code, but do not make it a requirement.
+* Each team can have at most one account on the evaluation server. Users that create multiple accounts to circumvent the rules will be excluded from the challenge.
+* Each team can submit at most three results during the challenge. 
+* Faulty submissions that return an error on HuggingFace do not count towards the submission limit.
+* Any attempt to circumvent these rules will result in a permanent ban of the team or company from the challenge.
+
 <p align="right">(<a href="#top">back to top</a>)</p>
 
 ## Evaluation Metrics
@@ -177,12 +189,10 @@ Please refer to [getting_started](docs/getting_started.md) for details.
 
 ### Submission site
 
-Please submit your result on our [evaluation server](https://huggingface.co/spaces/AGC2024-S/occupancy-and-flow-2024). The submission rule can be referred to [here](#rules-for-occupancy-challenge)
+Please submit your result on our [evaluation server](https://huggingface.co/spaces/AGC2024-S/occupancy-and-flow-2024). The submission rule can be referred to [here](#rules-for-occupancy-and-flow-challenge)
 
 ### Submission format
 
-We define a standardized 3D occupancy prediction result format that serves as an input to the evaluation code. Results are evaluated for each sample. The 3D occupancy prediction results for a the test evaluation set are stored in a folder. The participant needs to zip the results folder and submit it to the official evaluation server.
-
 The submission must be a single `dict` with the following structure:
 
 ```
@@ -194,17 +204,17 @@ submission = {
     'institution / company': "XXXXXXXXXX",
     'country / region': "XXXXXXX",
     'results': {
-        [scene_token1]: {
+        [sample_token1]: {
             'pcd_cls' (np.ndarray): predicted class ID of shape (N) and type np.uint8,
             'pcd_dist' (np.ndarray): predicted depth of shape (N) and type np.float16,
             'pcd_flow' (np.ndarray): predicted flow of shape (N, 2) and type np.float16,
         },
-        [scene_token2]: {
+        [sample_token2]: {
             'pcd_cls' (np.ndarray): predicted class ID of shape (N) and type np.uint8,
             'pcd_dist' (np.ndarray): predicted depth of shape (N) and type np.float16,
             'pcd_flow' (np.ndarray): predicted flow of shape (N, 2) and type np.float16,
         },
-        [scene_token3]: {
+        [sample_token3]: {
             'pcd_cls' (np.ndarray): predicted class ID of shape (N) and type np.uint8,
             'pcd_dist' (np.ndarray): predicted depth of shape (N) and type np.float16,
             'pcd_flow' (np.ndarray): predicted flow of shape (N, 2) and type np.float16,

docs/getting_started.md

@@ -37,7 +37,7 @@ export CUDA_VISIBLE_DEVICES=0
 
 ## Test Submission
 
-1. Fill your information in `projects/mmdet3d_plugin/datasets/nuscenes_occ.py` (Line 215).
+1. Fill your information in `projects/mmdet3d_plugin/datasets/nuscenes_occ.py` (Line 231).
 
 2. Test the baseline model on the test split with 8 GPUs, and generate the submission to the official evaluation server.