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README (1).md

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+---
+tags:
+- ultralyticsplus
+- yolov8
+- ultralytics
+- yolo
+- vision
+- object-detection
+- pytorch
+- awesome-yolov8-models
+- plant classification
+- plant detection
+- leaf classification
+- leaf detection
+- crop detection
+- disease detection
+library_name: ultralytics
+library_version: 8.0.43
+inference: False
+model-index:
+- name: foduucom/plant-leaf-detection-and-classification
+  results:
+  - task:
+      type: object-detection
+    metrics:
+    - type: precision
+      value: 0.946
+      name: [email protected](box)
+language:
+- en
+metrics:
+- accuracy
+---
+Below is the Model Card for the YOLOv8s Leaf Detection and Classification model:
+
+---
+<p align="center">
+<!-- Smaller size image -->
+<img src="https://huggingface.co/foduucom/leaf-detection-and-classification/resolve/main/thumbnail.jpg" alt="Image" style="width:500px; height:300px;">
+</p>
+# Model Card for YOLOv8s Leaf Detection and Classification
+
+## Model Summary
+
+The YOLOv8s Leaf Detection and Classification model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect and classify various types of leaves in images. The model has achieved a precision ([email protected]) of 0.946 on the object detection task.
+
+## Model Details
+
+### Model Description
+
+The YOLOv8s Leaf Detection and Classification model is built on the YOLOv8 architecture, which is known for its real-time object detection capabilities. This specific model has been trained to recognize and classify different types of leaves from various plant species. It can detect multiple leaf instances in an image and assign them to their respective classes.
+
+```
+['lemon', 'capcicum', 'pea', 'ornamaental', 'cassava', 'tomato', 'chiku', 'Blueberry', 'grape', 'cauliflower', 'potato', 'cardamom', 'cherry', 'garlic', 'gram', 'apple', 'tea', 'wheat', 'sunflower', 'chilli', 'cucumber', 'castor', 'coffee', 'soyabean', 'cabbage', 'olive', 'jamun', 'cotton', 'banana', 'peach', 'tobacco', 'mango', 'papaya', 'rose', 'brinjal', 'ginger', 'raspberry', 'gauava', 'rice', 'corn', 'strawberry', 'groundnut', 'sugarcane', 'pomgernate', 'onion']
+```
+- **Developed by:** FODUU AI
+- **Model type:** Object Detection
+- **Language(s) (NLP):** English
+  
+Furthermore, the YOLOv8s Leaf Detection and Classification model encourages user collaboration by allowing them to contribute their own plant leaf data. Users can submit images of new plant species, and suggest plant names for classification. Our team will diligently work to incorporate these new plant classes into the model, enhancing its ability to identify and classify an even wider variety of plant leaves.
+Users are invited to actively participate in expanding the YOLOv8s Leaf Detection and Classification model's capabilities by sharing their plant names and corresponding dataset links through our community platform or by emailing the information to [email protected]. Your contributions will play a crucial role in enriching the model's knowledge and recognition of diverse plant species.
+
+## Uses
+
+### Direct Use
+
+The YOLOv8s Leaf Detection and Classification model can be used directly for object detection tasks related to leaf detection and classification. It does not require fine-tuning or integration into a larger ecosystem or application.
+
+### Downstream Use 
+
+The model can also be fine-tuned for specific leaf detection and classification tasks or integrated into a larger application for plant-related research, agriculture, or environmental monitoring.
+
+### Out-of-Scope Use
+
+The model is not designed for unrelated tasks or object detection scenarios outside the scope of leaf detection and classification.
+
+## Bias, Risks, and Limitations
+
+The YOLOv8s Leaf Detection and Classification model may have some limitations and biases:
+
+- The model's performance may vary depending on the quality and diversity of the training data.
+- It may struggle with detecting leaves that are heavily occluded or overlapping with other objects.
+- The model's accuracy may be affected by variations in lighting conditions, image quality, and resolution.
+- It may not accurately detect very small or distant leaves in images.
+- The model's classification accuracy may be lower for leaf species that resemble each other closely.
+- The model's biases may stem from the biases present in the training data.
+
+### Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Further research and experimentation are recommended to assess its performance in specific use cases and domains.
+
+## How to Get Started with the Model
+
+To get started with the YOLOv8s Leaf Detection and Classification model, follow these steps:
+
+1. Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus) and [ultralytics](https://github.com/ultralytics/ultralytics) libraries using pip:
+
+```bash
+pip install ultralyticsplus ultralytics
+```
+
+2. Load the model and perform prediction using the provided code snippet.
+
+```python
+from ultralyticsplus import YOLO, render_result
+
+# load model
+model = YOLO('foduucom/plant-leaf-detection-and-classification')
+
+# set model parameters
+model.overrides['conf'] = 0.25  # NMS confidence threshold
+model.overrides['iou'] = 0.45  # NMS IoU threshold
+model.overrides['agnostic_nms'] = False  # NMS class-agnostic
+model.overrides['max_det'] = 1000  # maximum number of detections per image
+
+
+# set image
+image = 'path/to/your/image'
+
+# perform inference
+results = model.predict(image)
+
+# observe results
+print(results[0].boxes)
+render = render_result(model=model, image=image, result=results[0])
+render.show()
+```
+
+## Training Details
+
+
+### Training Data
+
+The model is trained on hundreds of images of 46 different plants, including both disease-infected and healthy leaves.
+
+### Training Procedure
+
+The training process involves using high GPU capacity and is run for up to 50 epochs, where each epoch represents a complete pass through the entire training dataset, adjusting model weights to minimize the classification loss and optimize the performance.
+
+#### Metrics
+
+- [email protected] (box): 
+0.946
+
+#### Summary
+
+YOLOv8s is a powerful convolutional neural network tailored for leaf detection and classification of over 46 plant species. It leverages a modified CSPDarknet53 backbone, self-attention mechanism, and a feature pyramid network for accurate multi-scaled object detection, providing precise identification and classification of plant leaves.
+
+### Model Architecture and Objective
+
+YOLOv8 architecture utilizes a modified CSPDarknet53 as its backbone with 53 convolutional layers and cross-stage partial connections for improved information flow. The head consists of convolutional and fully connected layers for predicting bounding boxes, objectness scores, and class probabilities. It incorporates a self-attention mechanism and a feature pyramid network for multi-scaled object detection, enabling focus on relevant image features and detecting objects of different sizes.
+
+### Compute Infrastructure
+
+#### Hardware
+
+NVIDIA A100 40GB GPU card
+
+#### Software
+Jupyter Notebook environment for model training.
+
+## Model Card Contact
+
+[email protected]
+```bibtex
+@ModelCard{
+  author    = {Nehul Agrawal and
+               Pranjal singh Thakur},
+  title     = { YOLOv8s Leaf Detection and Classification},
+  year      = {2023}
+}
+```
+---