Datasets:

devshaheen
/

100_crops_plants_object_detection_25k_image_dataset

@@ -1,175 +1,173 @@
 ---
 tags:
-- object-detection
-- agriculture
-- crops
-- plants
-- open-source
-- YOLOv5
----
-*Note: If you need this dataset in any other format, DM me on LinkedIn or ask in the discussions box. I will provide it ASAP.*
-# 100 Crops/Plants Object Detection 25K Image Dataset
-## Dataset Summary
-This dataset consists of **25,000+ high-quality annotated images** for **100 different crops/plants**, enabling object detection for agricultural AI applications. It is designed for training models in **YOLOv5 format** and has been **expertly annotated using Roboflow**.
-## Annotation and Format
-- The dataset was **annotated on Roboflow**, ensuring high-quality **bounding box annotations** for each crop/plant category.
-- All annotations follow the **YOLOv5 format**, making it easy to train models with YOLO-based architectures.
-- The dataset was **exported and preprocessed** into the required structure for direct training in YOLO-based object detection models.
-## Dataset Structure
-The dataset is split into three subsets:
-- **Train:** 17,553 images with labels
-- **Validation:** 4,990 images with labels
-- **Test:** 2,458 images with labels
-Each split contains:
-- **Images** (JPG/PNG format)
-- **Labels** (in YOLOv5 `.txt` format)
-- **`data.yaml`** configuration file
-### YOLOv5 Format
-Each image has a corresponding **text file** containing bounding box labels in the following format:
-```
-<class_id> <x_center> <y_center> <width> <height>
-```
-- All values are **normalized between 0 and 1**.
-- **Class ID** corresponds to one of the **100 crop/plant categories** listed below.
-## Plant/Crop Categories
-This dataset includes **100 different crops/plants**, providing valuable labeled images for agricultural AI applications. The dataset contains the following categories:
-1. Zingiber officinale (Ginger)
-2. Almonds
-3. Aloe Vera
-4. Apple
-5. Apricot
-6. Areca Nut
-7. Ashwagandha
-8. Avocado
-9. Bamboo
-10. Banana
-11. Beetroot
-12. Bell Pepper (Capsicum)
-13. Bitter Gourd
-14. Black Pepper
-15. Blackberry
-16. Blackgram
-17. Blueberry
-18. Bottle Gourd
-19. Brinjal (Eggplant)
-20. Broccoli
-21. Cabbage
-22. Cactus
-23. Cardamom
-24. Carrot
-25. Cashew
-26. Cassava
-27. Cauliflower
-28. Chamomile
-29. Cherry
-30. Chili Pepper
-31. Cinnamon
-32. Coconut
-33. Coffee Beans
-34. Coriander
-35. Cotton
-36. Cucumber
-37. Date Palm
-38. Dates
-39. Dragon Fruit
-40. Figs (Anjeer)
-41. Garlic
-42. Grapes
-43. Green Gram (Mung Bean)
-44. Groundnut (Peanut)
-45. Guava
-46. Jaggery
-47. Jute
-48. Kidney Bean
-49. Kiwi
-50. Lavender
-51. Lemon
-52. Lychee
-53. Maize
-54. Mango
-55. Mint Herb
-56. Mushroom
-57. Muskmelon
-58. Mustard Crop
-59. Oats
-60. Okra (Ladyfinger)
-61. Onion
-62. Orange
-63. Orchid (Orchidaceae)
-64. Papaya
-65. Pea
-66. Peach
-67. Pear
-68. Pineapple
-69. Pista (Pistachio)
-70. Plum
-71. Pomegranate
-72. Pomelo
-73. Potato
-74. Pumpkin
-75. Radish
-76. Raspberry
-77. Rice
-78. Rose
-79. Rosemary
-80. Rubber Plant
-81. Safflower
-82. Saffron
-83. Sesame
-84. Sorghum
-85. Soursop
-86. Soybean
-87. Spinach
-88. Starfruit (Carambola)
-89. Strawberry
-90. Sugar Apple
-91. Sugarcane
-92. Sunflower
-93. Sweet Potato
-94. Tea
-95. Tomato
-96. Tulip
-97. Turmeric
-98. Walnut
-99. Watermelon
-100. Wheat
-## Usage
-You can load this dataset using the Hugging Face `datasets` library:
-```python
-from datasets import load_dataset
-dataset = load_dataset("devshaheen/100_crops_plants_object_detection_25k_image_dataset")
-```
-Or, clone the dataset manually:
-```bash
-git clone https://huggingface.co/datasets/devshaheen/100_crops_plants_object_detection_25k_image_dataset
-```
-## License
-This dataset is released under the **MIT License**, allowing free use for both research and commercial projects. Please credit the authors when using it.
-## Credits
-Wherever this dataset is used, credits should be given to:
-- **Shaheen Nabi**: [LinkedIn](https://www.linkedin.com/in/shaheennabi/)
-- **Izhar Ashiq**: [LinkedIn](https://in.linkedin.com/in/izharashiq)
-## Contact
-For any inquiries, feel free to reach out via LinkedIn or start a discussion on Hugging Face.
-🔗 **Hugging Face Profile**: [https://huggingface.co/devshaheen](https://huggingface.co/devshaheen)
----
-Let's build the future of AI-powered agriculture together! 🚀🌱

 ---
+dataset_name: "100 Crops/Plants Object Detection 25K Image Dataset"
 tags:
+  - object-detection
+  - agriculture
+  - crops
+  - plants
+  - open-source
+  - YOLOv5
+language: "en"
+license: "MIT"
+size_categories:
+  - "25K+ images"
+task_categories:
+  - "object-detection"
+dataset_summary: |
+  This dataset consists of **25,000+ high-quality annotated images** of **100 different crops/plants**, designed to train object detection models for agricultural applications. The annotations follow the **YOLOv5 format**, making it easy to use for training deep learning models.
+dataset_structure: |
+  The dataset is provided in a **zip file**, which must be extracted before use. It is split into three subsets:
+  - **Train:** 17,553 images with labels
+  - **Validation:** 4,990 images with labels
+  - **Test:** 2,458 images with labels
+  Each split contains:
+  - **Images** (JPG/PNG format)
+  - **Labels** (YOLOv5 `.txt` format)
+  - **`data.yaml`** configuration file for YOLO training
+annotation_details: |
+  - **Annotated using Roboflow**
+  - **Bounding boxes for each crop/plant category**
+  - **Preprocessed for YOLOv5-based models**
+  - Each image has a corresponding `.txt` annotation file with bounding boxes in the following format:
+    ```
+    <class_id> <x_center> <y_center> <width> <height>
+    ```
+  - All values are **normalized between 0 and 1**.
+plant_categories: |
+  This dataset includes **100 crop/plant categories**, providing labeled images for agricultural AI applications:
+  1. Zingiber officinale (Ginger)
+  2. Almonds
+  3. Aloe Vera
+  4. Apple
+  5. Apricot
+  6. Areca Nut
+  7. Ashwagandha
+  8. Avocado
+  9. Bamboo
+  10. Banana
+  11. Beetroot
+  12. Bell Pepper (Capsicum)
+  13. Bitter Gourd
+  14. Black Pepper
+  15. Blackberry
+  16. Blackgram
+  17. Blueberry
+  18. Bottle Gourd
+  19. Brinjal (Eggplant)
+  20. Broccoli
+  21. Cabbage
+  22. Cactus
+  23. Cardamom
+  24. Carrot
+  25. Cashew
+  26. Cassava
+  27. Cauliflower
+  28. Chamomile
+  29. Cherry
+  30. Chili Pepper
+  31. Cinnamon
+  32. Coconut
+  33. Coffee Beans
+  34. Coriander
+  35. Cotton
+  36. Cucumber
+  37. Date Palm
+  38. Dates
+  39. Dragon Fruit
+  40. Figs (Anjeer)
+  41. Garlic
+  42. Grapes
+  43. Green Gram (Mung Bean)
+  44. Groundnut (Peanut)
+  45. Guava
+  46. Jaggery
+  47. Jute
+  48. Kidney Bean
+  49. Kiwi
+  50. Lavender
+  51. Lemon
+  52. Lychee
+  53. Maize
+  54. Mango
+  55. Mint Herb
+  56. Mushroom
+  57. Muskmelon
+  58. Mustard Crop
+  59. Oats
+  60. Okra (Ladyfinger)
+  61. Onion
+  62. Orange
+  63. Orchid (Orchidaceae)
+  64. Papaya
+  65. Pea
+  66. Peach
+  67. Pear
+  68. Pineapple
+  69. Pista (Pistachio)
+  70. Plum
+  71. Pomegranate
+  72. Pomelo
+  73. Potato
+  74. Pumpkin
+  75. Radish
+  76. Raspberry
+  77. Rice
+  78. Rose
+  79. Rosemary
+  80. Rubber Plant
+  81. Safflower
+  82. Saffron
+  83. Sesame
+  84. Sorghum
+  85. Soursop
+  86. Soybean
+  87. Spinach
+  88. Starfruit (Carambola)
+  89. Strawberry
+  90. Sugar Apple
+  91. Sugarcane
+  92. Sunflower
+  93. Sweet Potato
+  94. Tea
+  95. Tomato
+  96. Tulip
+  97. Turmeric
+  98. Walnut
+  99. Watermelon
+  100. Wheat
+usage: |
+  You can load this dataset using the Hugging Face `datasets` library:
+  ```python
+  from datasets import load_dataset
+  dataset = load_dataset("devshaheen/100_crops_plants_object_detection_25k_image_dataset")
+clone_dataset: |
+  Or, clone the dataset manually:
+  ```bash
+  git clone https://huggingface.co/datasets/devshaheen/100_crops_plants_object_detection_25k_image_dataset
+license_details: |
+  This dataset is released under the MIT License, allowing free use for both research and commercial projects. Please credit the authors when using it.
+citation: |
+  If you use this dataset, please credit:
+  - Shaheen Nabi ([LinkedIn](https://www.linkedin.com/in/shaheennabi/))
+  - Izhar Ashiq ([LinkedIn](https://in.linkedin.com/in/izharashiq))
+contact: |
+  For any inquiries, feel free to reach out via LinkedIn or start a discussion on Hugging Face.
+  🔗 Hugging Face Profile: [https://huggingface.co/devshaheen](https://huggingface.co/devshaheen)
+note: |
+  Note: If you need this dataset in any other format, DM me on LinkedIn or ask in the discussions box. I will provide it ASAP. 🚀🌱