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README.md

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-# Plant Species Classification Dataset
-
-A comprehensive dataset containing 64 different plant species with high-quality images for machine learning and computer vision applications.
-
-Model Trainig code and trainned model with detailed performance analysis is present on the Github
-https://github.com/jameelkhalidawan/Plant-Detection-Model-using-Yolo
-
-## Dataset Overview
-
-This dataset is designed for plant species classification tasks and contains images of various plant species organized in a structured format suitable for training deep learning models.
-
-### Key Statistics
-
-- **Total Classes**: 64 plant species
-- **Total Images**: 152,042 images
-- **Image Format**: JPG
-- **Dataset Split**:
-  - Training: 106,395 images
-  - Validation: 22,779 images  
-  - Test: 22,868 images
-
-## Dataset Structure
-
-```
-Plants_Datadet/
-├── train/                    # Training data (106,395 images)
-│   ├── [Plant Species 1]/    # Each folder contains images of one species
-│   ├── [Plant Species 2]/
-│   └── ...
-├── val/                      # Validation data (22,779 images)
-│   ├── [Plant Species 1]/
-│   ├── [Plant Species 2]/
-│   └── ...
-├── test/                     # Test data (22,868 images)
-│   ├── [Plant Species 1]/
-│   ├── [Plant Species 2]/
-│   └── ...
-├── train_split/              # Additional split for training (generated by training code)
-│   ├── train/
-│   ├── val/
-│   ├── train.cache
-│   └── val.cache
-└── image_counts.xlsx         # Detailed image count statistics
-```
-
-## Plant Species Included
-
-The dataset contains 64 diverse plant species including:
-
-### Trees and Shrubs
-- Acacia dealbata Link
-- Liriodendron tulipifera L
-- Nandina domestica Thunb
-- Pyracantha coccinea M.Roem
-- Schefflera arboricola (Hayata) Merr
-- Smilax aspera L
-- Trachelospermum jasminoides (Lindl.) Lem
-- Zamioculcas zamiifolia (Lodd.) Engl
-
-### Herbs and Wildflowers
-- Aegopodium podagraria L
-- Anemone alpina L
-- Anemone hepatica L
-- Anemone hupehensis (Lemoine) Lemoine
-- Anemone nemorosa L
-- Angelica sylvestris L
-- Barbarea vulgaris R.Br
-- Cirsium arvense (L.) Scop
-- Cirsium vulgare (Savi) Ten
-- Cymbalaria muralis P.Gaertn., B.Mey. & Scherb
-- Dryopteris filix-mas (L.) Schott
-- Epipactis helleborine (L.) Crantz
-- Fragaria vesca L
-- Helminthotheca echioides (L.) Holub
-- Humulus lupulus L
-- Hypericum androsaemum L
-- Hypericum calycinum L
-- Kniphofia uvaria (L.) Hook
-- Lactuca serriola L
-- Lamium album L
-- Lamium galeobdolon (L.) L
-- Lamium maculatum (L.) L
-- Lamium purpureum L
-- Lapsana communis L
-- Lupinus polyphyllus Lindl
-- Melilotus albus Medik
-- Mercurialis annua L
-- Nymphaea alba L
-- Ophrys apifera Huds
-- Pancratium maritimum L
-- Papaver rhoeas L
-- Papaver somniferum L
-- Perovskia atriplicifolia Benth
-- Trifolium incarnatum L
-
-### Succulents and Sedums
-- Sedum acre L
-- Sedum album L
-- Sedum rupestre L
-- Sedum sediforme (Jacq.) Pau
-
-### Ornamental Plants
-- Anthurium andraeanum Linden ex André
-- Fittonia albivenis (Lindl. ex Veitch) Brummitt
-- Lavandula angustifolia Mill
-- Lavandula stoechas L
-- Pelargonium graveolens L'Hér
-- Pelargonium inquinans (L.) Aiton
-- Pelargonium zonale (L.) L'Hér
-- Pelargonium zonale (L.) L'Hér. ex Aiton
-- Punica granatum L
-- Tagetes erecta L
-- Tagetes patula L
-
-### Vegetables and Fruits
-- Cucurbita maxima Duchesne
-- Cucurbita pepo L
-
-### Tradescantia Varieties
-- Tradescantia fluminensis Vell
-- Tradescantia pallida (Rose) D.R.Hunt
-- Tradescantia spathacea Sw
-- Tradescantia virginiana L
-- Tradescantia zebrina Bosse
-
-## Image Characteristics
-
-- **Format**: JPG
-- **Naming Convention**: Images use hash-based filenames (e.g., `0038b49e0352646885a8899be350813d927f34a5.jpg`)
-- **Quality**: High-quality images suitable for detailed plant identification
-- **Content**: Various parts of plants including flowers, leaves, stems, and full plant views
-
-## Usage
-
-This dataset is suitable for:
-
-1. **Plant Species Classification**: Train models to identify and classify different plant species
-2. **Computer Vision Research**: Develop and test image classification algorithms
-3. **Botanical Studies**: Analyze plant characteristics and features
-4. **Educational Applications**: Create learning tools for plant identification
-5. **Agricultural Applications**: Assist in crop and weed identification
-
-
-## Dataset Splits
-
-The dataset is pre-split into training, validation, and test sets to ensure proper model evaluation:
-
-- **Training Set**: Used for model training and parameter optimization
-- **Validation Set**: Used for hyperparameter tuning and model selection
-- **Test Set**: Used for final model evaluation and performance assessment
-
-## Additional Files
-
-- `image_counts.xlsx`: Contains detailed statistics about the number of images per class
-- `train_split/`: Contains additional splits generated during the training process with cache files for faster data loading
-
-## Citation
-
-If you use this dataset in your research or projects, please cite it appropriately and acknowledge the contributors.
-
-## License
-
-Please check the license terms before using this dataset for commercial purposes.
-
----
-
+# Plant Species Classification Dataset
+
+A comprehensive dataset containing 64 different plant species with high-quality images for machine learning and computer vision applications.
+Model Trainig code and trainned model with detailed performance analysis is present on the Github
+https://github.com/jameelkhalidawan/Plant-Detection-Model-using-Yolo
+
+## Dataset Overview
+
+This dataset is designed for plant species classification tasks and contains images of various plant species organized in a structured format suitable for training deep learning models.
+
+### Key Statistics
+
+- **Total Classes**: 64 plant species
+- **Total Images**: 152,042 images
+- **Image Format**: JPG
+- **Dataset Split**:
+  - Training: 106,395 images
+  - Validation: 22,779 images  
+  - Test: 22,868 images
+
+## Dataset Structure
+
+```
+Plants_Datadet/
+├── train/                    # Training data (106,395 images)
+│   ├── [Plant Species 1]/    # Each folder contains images of one species
+│   ├── [Plant Species 2]/
+│   └── ...
+├── val/                      # Validation data (22,779 images)
+│   ├── [Plant Species 1]/
+│   ├── [Plant Species 2]/
+│   └── ...
+├── test/                     # Test data (22,868 images)
+│   ├── [Plant Species 1]/
+│   ├── [Plant Species 2]/
+│   └── ...
+├── train_split/              # Additional split for training (generated by training code)
+│   ├── train/
+│   ├── val/
+│   ├── train.cache
+│   └── val.cache
+└── image_counts.xlsx         # Detailed image count statistics
+```
+
+## Plant Species Included
+
+The dataset contains 64 diverse plant species including:
+
+### Trees and Shrubs
+- Acacia dealbata Link
+- Liriodendron tulipifera L
+- Nandina domestica Thunb
+- Pyracantha coccinea M.Roem
+- Schefflera arboricola (Hayata) Merr
+- Smilax aspera L
+- Trachelospermum jasminoides (Lindl.) Lem
+- Zamioculcas zamiifolia (Lodd.) Engl
+
+### Herbs and Wildflowers
+- Aegopodium podagraria L
+- Anemone alpina L
+- Anemone hepatica L
+- Anemone hupehensis (Lemoine) Lemoine
+- Anemone nemorosa L
+- Angelica sylvestris L
+- Barbarea vulgaris R.Br
+- Cirsium arvense (L.) Scop
+- Cirsium vulgare (Savi) Ten
+- Cymbalaria muralis P.Gaertn., B.Mey. & Scherb
+- Dryopteris filix-mas (L.) Schott
+- Epipactis helleborine (L.) Crantz
+- Fragaria vesca L
+- Helminthotheca echioides (L.) Holub
+- Humulus lupulus L
+- Hypericum androsaemum L
+- Hypericum calycinum L
+- Kniphofia uvaria (L.) Hook
+- Lactuca serriola L
+- Lamium album L
+- Lamium galeobdolon (L.) L
+- Lamium maculatum (L.) L
+- Lamium purpureum L
+- Lapsana communis L
+- Lupinus polyphyllus Lindl
+- Melilotus albus Medik
+- Mercurialis annua L
+- Nymphaea alba L
+- Ophrys apifera Huds
+- Pancratium maritimum L
+- Papaver rhoeas L
+- Papaver somniferum L
+- Perovskia atriplicifolia Benth
+- Trifolium incarnatum L
+
+### Succulents and Sedums
+- Sedum acre L
+- Sedum album L
+- Sedum rupestre L
+- Sedum sediforme (Jacq.) Pau
+
+### Ornamental Plants
+- Anthurium andraeanum Linden ex André
+- Fittonia albivenis (Lindl. ex Veitch) Brummitt
+- Lavandula angustifolia Mill
+- Lavandula stoechas L
+- Pelargonium graveolens L'Hér
+- Pelargonium inquinans (L.) Aiton
+- Pelargonium zonale (L.) L'Hér
+- Pelargonium zonale (L.) L'Hér. ex Aiton
+- Punica granatum L
+- Tagetes erecta L
+- Tagetes patula L
+
+### Vegetables and Fruits
+- Cucurbita maxima Duchesne
+- Cucurbita pepo L
+
+### Tradescantia Varieties
+- Tradescantia fluminensis Vell
+- Tradescantia pallida (Rose) D.R.Hunt
+- Tradescantia spathacea Sw
+- Tradescantia virginiana L
+- Tradescantia zebrina Bosse
+
+## Image Characteristics
+
+- **Format**: JPG
+- **Naming Convention**: Images use hash-based filenames (e.g., `0038b49e0352646885a8899be350813d927f34a5.jpg`)
+- **Quality**: High-quality images suitable for detailed plant identification
+- **Content**: Various parts of plants including flowers, leaves, stems, and full plant views
+
+## Usage
+
+This dataset is suitable for:
+
+1. **Plant Species Classification**: Train models to identify and classify different plant species
+2. **Computer Vision Research**: Develop and test image classification algorithms
+3. **Botanical Studies**: Analyze plant characteristics and features
+4. **Educational Applications**: Create learning tools for plant identification
+5. **Agricultural Applications**: Assist in crop and weed identification
+
+
+## Dataset Splits
+
+The dataset is pre-split into training, validation, and test sets to ensure proper model evaluation:
+
+- **Training Set**: Used for model training and parameter optimization
+- **Validation Set**: Used for hyperparameter tuning and model selection
+- **Test Set**: Used for final model evaluation and performance assessment
+
+## Additional Files
+
+- `image_counts.xlsx`: Contains detailed statistics about the number of images per class
+- `train_split/`: Contains additional splits generated during the training process with cache files for faster data loading
+
+## Citation
+
+If you use this dataset in your research or projects, please cite it appropriately and acknowledge the contributors.
+
+## License
+
+Please check the license terms before using this dataset for commercial purposes.
+
+---
+
 *This dataset provides a comprehensive collection of plant species images suitable for various machine learning and computer vision applications in botany, agriculture, and environmental studies.*