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data_curation.py

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+"""
+This script is used to curate the data for the project. 
+
+Implement your functions to to clean the data and prepare it for model training.
+
+Note: the competition requires that you use FiftyOne for data curation and you are only allowed to
+use the approaved dataset from the hub, Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set, which can 
+be found here: https://huggingface.co/datasets/Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set
+"""
+
+import fiftyone as fo
+import fiftyone.utils.huggingface as fouh
+
+# Implement functions for data curation. below are just dummy functions as examples
+
+def shuffle_data(dataset):
+    """Shuffle the dataset"""
+    return dataset.shuffle(seed=51)
+
+def take_random_sample(dataset):
+    """Take a sample from the dataset"""
+    return dataset.take(size=10,seed=51)
+
+def prepare_dataset(name):
+    """
+    Prepare the dataset for model training.
+    
+    Args:
+        name (str): The name of the dataset to load. Must be "Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set".
+    
+    Returns:
+        fiftyone.core.dataset.Dataset: The curated dataset.
+    
+    Raises:
+        ValueError: If the provided dataset name is not the approved one.
+    
+    Note:
+        The following code block MUST NOT be removed from your submission:
+        
+        APPROVED_DATASET = "Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set"
+        
+        if name != APPROVED_DATASET:
+            raise ValueError(f"Only the approved dataset '{APPROVED_DATASET}' is allowed for this competition.")
+        
+        This ensures that only the approved dataset is used for the competition.
+    """
+    APPROVED_DATASET = "Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set"
+    Vox
+    if name != APPROVED_DATASET:
+        raise ValueError(f"Only the approved dataset '{APPROVED_DATASET}' is allowed for this competition.")
+    
+    # Load the approved dataset from the hub
+    dataset = fouh.load_from_hub(name, split="train")
+    
+    # Implement your data curation functions here
+    dataset = shuffle_data(dataset)
+    dataset = take_random_sample(dataset)
+    
+    # Return the curated dataset
+    curated_dataset = dataset.clone() 
+    return curated_dataset

training_config.yaml

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+# Dataset split
+train_split: 0.9
+val_split: 0.1
+
+# Training parameters
+train_params:
+  epochs: 50
+  batch: 16
+  imgsz: 640
+  lr0: 0.01
+  lrf: 0.01