Upload folder using huggingface_hub

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+claris_curated_dataset.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,66 @@
+---
+language:
+  - en
+tags:
+  - sign language recognition
+  - emergency response
+  - computer vision
+---
+
+# CLARIS - Critical Emergency Sign Language Dataset
+
+This dataset is a curated subset of the "Google - Isolated Sign Language Recognition" dataset, specifically filtered for the **CLARIS (Clear and Live Automated Response for Inclusive Safety)** project.
+
+## Dataset Description
+
+The primary goal of the CLARIS project is to develop a mobile application that provides a lifeline for the Deaf community during emergencies. This dataset was created to train a proof-of-concept AI model capable of recognizing a vocabulary of critical emergency-related signs.
+
+The data consists of pre-extracted landmark coordinates from video clips of isolated signs. It originates from the [Google - Isolated Sign Language Recognition Kaggle Competition](https://www.kaggle.com/competitions/asl-signs).
+
+## Dataset Structure
+
+The dataset is provided in both CSV and Parquet (coming soon) formats. Each row represents the coordinates of a single landmark in a single frame of a video sequence.
+
+| Column           | Dtype   | Description                                                                 |
+| ---------------- | ------- | --------------------------------------------------------------------------- |
+| `frame`          | int16   | The frame number within the sequence.                                       |
+| `row_id`         | object  | A unique identifier for the landmark within the frame.                      |
+| `type`           | object  | The type of landmark (`face`, `left_hand`, `right_hand`, `pose`).             |
+| `landmark_index` | int16   | The index of the landmark within its type.                                  |
+| `x`              | float64 | The normalized x-coordinate of the landmark.                                |
+| `y`              | float64 | The normalized y-coordinate of the landmark.                                |
+| `z`              | float64 | The normalized z-coordinate of the landmark (depth).                        |
+| `path`           | object  | The path to the original source parquet file for the sequence.              |
+| `participant_id` | int64   | A unique identifier for the participant (signer).                           |
+| `sequence_id`    | int64   | A unique identifier for the sign sequence.                                  |
+| `sign`           | object  | The ground truth label for the sign being performed.                        |
+
+## Curation Process
+
+To create a focused dataset for our specific use case, we performed a two-step curation process:
+
+1.  **Vocabulary Filtering:** We selected **62 signs** deemed most relevant for describing medical, fire, or intruder emergencies.
+2.  **Participant Filtering:** To create a manageable dataset for rapid prototyping, we constrained the data to sequences from **two distinct participants** who had a balanced distribution of the target signs.
+
+This process resulted in a final dataset containing **1,719 unique sign sequences**, comprising over 37 million landmark rows.
+
+## Usage
+
+We recommend using the Parquet file for faster loading times.
+
+```python
+import pandas as pd
+
+# Load the full curated dataset
+df = pd.read_parquet('claris_curated_dataset.parquet')
+
+# Or load the smaller, subsampled version
+df_sample = pd.read_parquet('claris_subsample_dataset.parquet')
+
+print(df.head())
+```
+
+## Link to Project Notebook
+
+The complete methodology, including data preprocessing, model training, and analysis, can be found in our Kaggle notebook:
+https://www.kaggle.com/code/eveelyn/datathon2025-dem

claris_curated_dataset.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46d6eb5d6732829787388e6c076bd6d43856a75a2ac3416671b5fb939da81f4e
+size 5502322614

dataset_infos.json

@@ -0,0 +1,64 @@
+{
+  "claris_emergency_signs": {
+    "description": "A curated subset of the Google Isolated Sign Language Recognition dataset, filtered to 62 critical emergency-related signs from two participants.",
+    "citation": "@misc{claris_dataset_2024, author={Dama D. Daliman, Evelyn Yosiana, Micky Valentino}, title={CLARIS - Critical Emergency Sign Language Dataset}, year={2024}, publisher={Hugging Face}, url={[Link to your Hugging Face Repo]}}",
+    "homepage": "[Link to your Hugging Face Repo]",
+    "license": "mit",
+    "features": {
+      "frame": {
+        "dtype": "int16",
+        "_type": "Value"
+      },
+      "row_id": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "type": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "landmark_index": {
+        "dtype": "int16",
+        "_type": "Value"
+      },
+      "x": {
+        "dtype": "float64",
+        "_type": "Value"
+      },
+      "y": {
+        "dtype": "float64",
+        "_type": "Value"
+      },
+      "z": {
+        "dtype": "float64",
+        "_type": "Value"
+      },
+      "path": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "participant_id": {
+        "dtype": "int64",
+        "_type": "Value"
+      },
+      "sequence_id": {
+        "dtype": "int64",
+        "_type": "Value"
+      },
+      "sign": {
+        "dtype": "string",
+        "_type": "Value"
+      }
+    },
+    "splits": {
+      "train": {
+        "name": "train",
+        "num_bytes": 5120000000,
+        "num_examples": 1719,
+        "dataset_name": "claris_emergency_signs"
+      }
+    },
+    "download_size": 5120000000,
+    "dataset_size": 5120000000
+  }
+}

vocabulary.txt

@@ -0,0 +1,60 @@
+bad
+can
+close
+cry
+cut
+down
+drop
+fall
+fast
+find
+give
+go
+have
+haveto
+hear
+hide
+high
+hot
+jump
+look
+loud
+mad
+no
+not
+now
+open
+owie
+quiet
+see
+sick
+stuck
+talk
+time
+touch
+up
+wait
+will
+yes
+arm
+child
+dad
+eye
+face
+fireman
+head
+hesheit
+man
+mom
+person
+police
+car
+glasswindow
+home
+outside
+room
+stairs
+water
+where
+who
+why