Update script.py

script.py

@@ -93,76 +93,6 @@ def train_model(training_dataset, training_config):
     return best_model
 
 
-def run_inference_on_eval_set(eval_dataset, best_model):
-    """
-    Run inference on the evaluation set using the best trained model.
-
-    Args:
-        eval_dataset (fiftyone.core.dataset.Dataset): The evaluation dataset.
-        best_model (YOLO): The best trained YOLO model.
-
-    Returns:
-        The dataset eval_dataset with predictions
-    """
-    eval_dataset.apply_model(best_model, label_field="predictions")
-    eval_dataset.save()
-    return eval_dataset
-
-
-def eval_model(dataset_to_evaluate):
-    """
-    Evaluate the model on the evaluation dataset.
-
-    Args:
-        dataset_to_evaluate (fiftyone.core.dataset.Dataset): The evaluation dataset.
-
-    Returns:
-        the mean average precision (mAP) of the model on the evaluation dataset.
-    """
-    current_datetime = datetime.now().strftime("%Y%m%d_%H%M%S")
-
-    detection_results = dataset_to_evaluate.evaluate_detections(
-        gt_field="ground_truth",  
-        pred_field="predictions",
-        eval_key=f"evalrun_{current_datetime}",
-        compute_mAP=True,
-        )
-
-    return detection_results.mAP()
-
-def run():
-    """
-    Main function to run the entire training and evaluation process.
-
-    Returns:
-        None
-    """
-    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    config_path = os.path.join(script_dir, 'training_config.yaml')
-    with open(config_path, 'r') as file:
-        training_config = yaml.safe_load(file)
-
-    #train set
-    curated_train_dataset = prepare_dataset(name="Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set")
-
-    #public eval set
-    public_eval_dataset = fouh.load_from_hub("Voxel51/DCVAI-Challenge-Public-Eval-Set")
-
-    N = len(curated_train_dataset)
-    
-    best_trained_model = train_model(training_dataset=curated_train_dataset, training_config=training_config)
-    
-    model_predictions = run_inference_on_eval_set(eval_dataset=public_eval_dataset, best_model=best_trained_model)
-    
-    mAP_on_public_eval_set = eval_model(dataset_to_evaluate=model_predictions)
-
-    adjusted_mAP = (mAP_on_public_eval_set * log(N))/N
-
-    # need to add logic to log the score to the leaderboard for now, just print
-
-    print(f"The adjusted mean Average Precision (mAP) on the public evaluation set is: {adjusted_mAP:.4f}")
-
 
 if __name__=="__main__":
     run()