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wilgomoreira
/
dataset_vineyardLogits_sigmoid

Tasks:
Image Segmentation
Tags:
deep-learning
agriculture
vineyards
segmentation
logits
License:
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README.md ADDED
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+ ---
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+ tags:
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+ - deep-learning
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+ - agriculture
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+ - vineyards
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+ - segmentation
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+ - logits
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+ license: mit
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+ datasets:
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+ - dataset_vineyardLogits_sigmoid
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+ task_categories:
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+ - image-segmentation
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+ ---
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+ # Vineyard Logits Sigmoid Dataset 🍇
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+
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+ ## 📌 Overview
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+ The **dataset_vineyardLogits_sigmoid** is a collection of **logits and labels** used for training and testing deep learning models in **precision agriculture**.
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+
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+ 💡 **Key Details**:
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+ - **Binary classification task** with **one class**.
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+ - **Sigmoid activation function** used to output probabilities.
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+ - **Optimized for distinguishing vine plants from background elements**.
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+
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+ This dataset provides valuable logits from models trained on vineyard segmentation tasks, enabling further research and development in precision agriculture.
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+
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+ ---
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+
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+ ## 📊 Hyperparameters
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+
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+ The dataset consists of **three distinct datasets** used for **binary classification**. Below are the key hyperparameters used during training and testing:
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+
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+ 1. **Split Ratio**
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+ - The dataset is split **80:20** (80% training, 20% testing).
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+
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+ 2. **Learning Rate**
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+ - Initial **learning rate: 0.001**.
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+
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+ 3. **Batch Sizes**
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+ - **Training batch size**: **30**
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+ - **Testing batch size**: **3**
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+ - This ensures efficient model training and evaluation.
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+
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+ ---
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+
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+ ## 📂 Dataset Structure
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+
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+ ```plaintext
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+ dataset_vineyardLogits_sigmoid
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+ ├── deeplab_EARLY_FUSION_t1
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+ ├── deeplab_EARLY_FUSION_t2
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+ ├── deeplab_EARLY_FUSION_t3
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+ ├── deeplab_GNDVI_t1
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+ ├── deeplab_GNDVI_t2
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+ ├── deeplab_GNDVI_t3
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+ ├── deeplab_NDVI_t1
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+ ├── deeplab_NDVI_t2
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+ ├── deeplab_NDVI_t3
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+ ├── deeplab_RGB_t1
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+ ├── deeplab_RGB_t2
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+ ├── deeplab_RGB_t3
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+ ├── segnet_EARLY_FUSION_t1
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+ ├── segnet_EARLY_FUSION_t2
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+ ├── segnet_EARLY_FUSION_t3
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+ ├── segnet_GNDVI_t1
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+ ├── segnet_GNDVI_t2
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+ ├── segnet_GNDVI_t3
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+ ├── segnet_NDVI_t1
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+ ├── segnet_NDVI_t2
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+ ├── segnet_NDVI_t3
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+ ├── segnet_RGB_t1
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+ ├── segnet_RGB_t2
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+ ├── segnet_RGB_t3
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+ └── README.md
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+ ```
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+
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+ ---
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+
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+ ## 📑 Contents
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+
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+ - **model_modality_fold_n/pred_masks_train**: Logits from the training set.
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+ - **model_modality_fold_n/pred_masks_test**: Logits from the test set.
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+
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+ ---
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+
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+ ## 📸 Data Description
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+
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+ - **Model Logits**
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+ 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**.
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+
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+ - **Original Images**
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+ The images originate from aerial multispectral imagery collected from **three vineyards in central Portugal**:
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+ - **Quinta de Baixo (QTA)**
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+ - **ESAC**
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+ - **Valdoeiro (VAL)**
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+
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+ ✅ **Captured at 240x240 resolution** using:
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+ - **X7 RGB camera**
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+ - **MicaSense Altum multispectral sensor**
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+
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+ ✅ Includes **RGB and Near-Infrared (NIR) bands**, enabling vegetation indices like **NDVI** and **GNDVI**.
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+ ✅ **Ground-truth annotations available** for vineyard segmentation.
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+
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+ 📌 **For more details**, refer to the dataset:
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+ [Cybonic, "DL Vineyard Segmentation Study," v1.0, GitHub, 2024](https://github.com/Cybonic/DL_vineyard_segmentation_study)
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+
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+ ---
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+
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+ ## 📥 How to Use
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+
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+ ### **1️⃣ Load in Python**
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+ To load the dataset directly from Hugging Face:
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+
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+ ```python
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+ from datasets import load_dataset
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+ dataset = load_dataset("wilgomoreira/dataset_vineyardLogits_sigmoid")
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+ print(dataset)
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+ ```
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+
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+ ### **2️⃣ Download Specific Files**
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+ To download a specific file:
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+
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+ ```bash
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+ wget https://huggingface.co/datasets/seu-usuario/dataset_vineyardLogits_sigmoid/resolve/main/logits_train.npz
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+ ```
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+
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+ ---
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+
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+ ## 🛠 License
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+ This dataset is released under the **MIT License**.
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+ Please make sure to comply with the license terms when using this dataset.
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+
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+ ---
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+
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+ ## 🙌 Acknowledgments
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+ This dataset was created by **Wilgo Cardoso** for research in **precision agriculture and deep learning segmentation**.
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+
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+ ---
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+
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+ ## 📧 Contact
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+ For any questions or collaborations, please contact:
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+ ✉️ **[email protected]**
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