wilgomoreira/dataset_vineyardLogits_sigmoid

Vineyard Logits Sigmoid Dataset 🍇

📌 Overview

The dataset_vineyardLogits_sigmoid is a collection of logits and labels used for training and testing deep learning models in precision agriculture.

💡 Key Details:

• Binary classification task with one class.
• Sigmoid activation function used to output probabilities.
• Optimized for distinguishing vine plants from background elements.

This dataset provides valuable logits from models trained on vineyard segmentation tasks, enabling further research and development in precision agriculture.

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📊 Hyperparameters

The dataset consists of three distinct datasets used for binary classification. Below are the key hyperparameters used during training and testing:

1. Split Ratio

   • The dataset is split 80:20 (80% training, 20% testing).

2. Learning Rate

   • Initial learning rate: 0.001.

3. Batch Sizes

   • Training batch size: 30
   • Testing batch size: 3
   • This ensures efficient model training and evaluation.

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📂 Dataset Structure

dataset_vineyardLogits_sigmoid
├── deeplab_EARLY_FUSION_t1
├── deeplab_EARLY_FUSION_t2
├── deeplab_EARLY_FUSION_t3
├── deeplab_GNDVI_t1
├── deeplab_GNDVI_t2
├── deeplab_GNDVI_t3
├── deeplab_NDVI_t1
├── deeplab_NDVI_t2
├── deeplab_NDVI_t3
├── deeplab_RGB_t1
├── deeplab_RGB_t2
├── deeplab_RGB_t3
├── segnet_EARLY_FUSION_t1
├── segnet_EARLY_FUSION_t2
├── segnet_EARLY_FUSION_t3
├── segnet_GNDVI_t1
├── segnet_GNDVI_t2
├── segnet_GNDVI_t3
├── segnet_NDVI_t1
├── segnet_NDVI_t2
├── segnet_NDVI_t3
├── segnet_RGB_t1
├── segnet_RGB_t2
├── segnet_RGB_t3
└── README.md

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📑 Contents

• model_modality_fold_n/pred_masks_train: Logits from the training set.
• model_modality_fold_n/pred_masks_test: Logits from the test set.

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📸 Data Description

• Model Logits
  The dataset consists of logits generated by DeepLabV3 and SegNet during training and testing. These logits are unnormalized raw scores before applying the sigmoid activation function.

• Original Images
  The images originate from aerial multispectral imagery collected from three vineyards in central Portugal:

  • Quinta de Baixo (QTA)
  • ESAC
  • Valdoeiro (VAL)

  ✅ Captured at 240x240 resolution using:

  • X7 RGB camera
  • MicaSense Altum multispectral sensor

  ✅ Includes RGB and Near-Infrared (NIR) bands, enabling vegetation indices like NDVI and GNDVI.
  ✅ Ground-truth annotations available for vineyard segmentation.

📌 For more details, refer to the dataset:
Cybonic, "DL Vineyard Segmentation Study," v1.0, GitHub, 2024

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📥 How to Use

1️⃣ Load in Python

To load the dataset directly from Hugging Face:

from datasets import load_dataset
dataset = load_dataset("wilgomoreira/dataset_vineyardLogits_sigmoid")
print(dataset)

2️⃣ Download Specific Files

To download a specific file:

wget https://huggingface.co/datasets/seu-usuario/dataset_vineyardLogits_sigmoid/resolve/main/logits_train.npz

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🛠 License

This dataset is released under the MIT License.
Please make sure to comply with the license terms when using this dataset.

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🙌 Acknowledgments

This dataset was created by Wilgo Cardoso for research in precision agriculture and deep learning segmentation.

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📧 Contact

For any questions or collaborations, please contact:
✉️ [email protected]