Update README.md

README.md

@@ -307,8 +307,8 @@ The repository is organized as follows:
 Each label file in the `labels/` directory follows this format:
 
 ```
-path,label,start,end,subject,cam
-path/to/clip,class_id,start_time,end_time,subject_id,camera_id
+path,label,start,end,subject,cam,dataset
+path/to/clip,class_id,start_time,end_time,subject_id,camera_id,dataset_name
 ```
 
 Where:
@@ -328,12 +328,14 @@ Where:
 - `end`: End time of the segment (in seconds)
 - `subject`: Subject ID
 - `cam`: Camera view ID
+- `dataset`: Name of the dataset
 
-For OOPS-Fall, only fall segments and non-fall segments are labeled; non-falls are labels as "other", independent of the underlying content, as long as it is not a fall.
+For OOPS-Fall, only fall segments and non-fall segments are labeled; non-falls are labels as "other", independent of the underlying content, as long as it is not a fall. 
+Cam and subject ids in OOPS-Fall are -1.
 
 ### Split Format
 
-Split files in the `splits/` directory list the video segments included in each partition:
+Split files in the `splits/` directory list the video segments included in each partition. You can use the split paths to filter the label data.:
 
 ```
 path
@@ -345,7 +347,7 @@ path/to/clip
 We provide multiple evaluation configurations via the `dataset.yaml` file:
 
 ### Basic Configurations
-- `default`: Access to all dataset labels
+- `default`: Access to all dataset labels (huggingface loads everything into the `train` split by default.)
 - `cs`: Cross-subject splits for all datasets
 - `cv`: Cross-view splits for all datasets
 
@@ -359,77 +361,6 @@ We provide multiple evaluation configurations via the `dataset.yaml` file:
 - `cs-staged-wild`: Train and validate on staged datasets with cross-subject splits, test on OOPS-Fall
 - `cv-staged-wild`: Train and validate on staged datasets with cross-view splits, test on OOPS-Fall
 
-## Usage
-
-To use this dataset with the Hugging Face datasets library:
-
-```python
-from datasets import load_dataset
-
-# Load the entire dataset with default configuration
-dataset = load_dataset("omnifall")
-
-# Use cross-subject (CS) evaluation protocol
-cs_dataset = load_dataset("omnifall", "cs")
-print(f"Train: {len(cs_dataset['train'])} samples")
-print(f"Validation: {len(cs_dataset['validation'])} samples")
-print(f"Test: {len(cs_dataset['test'])} samples")
-
-# Use cross-view (CV) evaluation protocol
-cv_dataset = load_dataset("omnifall", "cv")
-
-# Use staged-to-wild evaluation protocol (train on staged datasets, test on OOPS)
-staged_to_wild = load_dataset("omnifall", "cs-staged-wild")
-
-# Use individual dataset
-cmdfall = load_dataset("omnifall", "cmdfall")
-
-# Access specific fields from the dataset
-for item in dataset["train"][:5]:
-    print(f"Path: {item['path']}, Label: {item['label']}")
-```
-
-## Experiment Examples
-
-### Cross-Subject Fall Detection
-
-```python
-from datasets import load_dataset
-import torch
-from torch.utils.data import DataLoader
-
-# Load the cross-subject evaluation protocol
-dataset = load_dataset("omnifall", "cs-staged")
-
-# Preprocess and create dataloaders
-def preprocess(examples):
-    # Your preprocessing code here
-    return examples
-
-processed_dataset = dataset.map(preprocess, batched=True)
-train_dataloader = DataLoader(processed_dataset["train"], batch_size=32, shuffle=True)
-val_dataloader = DataLoader(processed_dataset["validation"], batch_size=32)
-test_dataloader = DataLoader(processed_dataset["test"], batch_size=32)
-
-# Train and evaluate your model
-```
-
-### Staged-to-Wild Generalization
-
-```python
-from datasets import load_dataset
-
-# Load the staged-to-wild evaluation protocol
-dataset = load_dataset("omnifall", "cs-staged-wild")
-
-# Train on staged data
-train_data = dataset["train"]
-val_data = dataset["validation"]
-
-# Evaluate on wild data
-wild_test_data = dataset["test"]
-```
-
 ## Citation
 
 If you use OmniFall in your research, please cite our paper (will be updated soon):