Update README.md

README.md

@@ -25,13 +25,13 @@ model-index:
       value: 0.835
       name: Top1_acc
 ---
-#Welcome to the repository of our state-of-the-art image classification model, uniquely fine-tuned on the robust architecture of YOLOv8s, tailored to distinguish between defective and good tires with unprecedented accuracy.
+#Welcome to the repository of our state-of-the-art image classification model, uniquely fine-tuned on the robust architecture of YOLOv8s, tailored to distinguish between defective and good tyres with unprecedented accuracy.
 
 # Model Details
 
 ## Model Description
 
-Our model leverages the cutting-edge capabilities of YOLOv8s, renowned for its speed and precision in object detection, which has been meticulously fine-tuned for the specific domain of tire quality assessment. This model emerges as an indispensable tool for automating quality control in tire manufacturing, ensuring that every tire meets the highest standards of safety and performance.
+Our model leverages the cutting-edge capabilities of YOLOv8s, renowned for its speed and precision in object detection, which has been meticulously fine-tuned for the specific domain of tyre quality assessment. This model emerges as an indispensable tool for automating quality control in tyre manufacturing, ensuring that every tyre meets the highest standards of safety and performance.
 
 - **Developed by:** FODUU AI
 - **Model type:** Image Classification
@@ -46,23 +46,40 @@ Our model leverages the cutting-edge capabilities of YOLOv8s, renowned for its s
 
 
 ## Key Features:
-- High Precision Classification: Classifies tires with a high degree of accuracy, reducing the margin of error significantly compared to traditional methods.
+- High Precision Classification: Classifies tyres with a high degree of accuracy, reducing the margin of error significantly compared to traditional methods.
 - Rapid Assessment: Optimized for quick image processing, allowing for real-time quality control on the production line.
-- Robust Training: Trained on a diverse dataset of tire images, capturing a wide range of defects to ensure reliability in various operational environments.
+- Robust Training: Trained on a diverse dataset of tyre images, capturing a wide range of defects to ensure reliability in various operational environments.
 - Easy Integration: Designed to seamlessly fit into existing manufacturing systems, facilitating a smooth transition from manual to automated quality assessment.
 
-## How to Use This Model
-This model is hosted on Hugging Face, making it incredibly easy to integrate and deploy. You can directly use our pre-trained model for classifying your tire images by following the instructions in our usage documentation.
 
+# Model Training Details
+
+## Dataset Description
+Our model was trained on a comprehensive and proprietary dataset consisting of several images, each meticulously labeled as 'defective' or 'good'. The dataset encompasses a variety of tyre types and conditions to ensure broad applicability and robustness. It includes:
+
+Images of good tyres: High-quality images captured under various lighting and background conditions to simulate real-world scenarios.
+Images of defective tyres: A diverse range of defects such as tread wear, sidewall bulges, and punctures, among others, were included to train the model effectively.
+
+## Preprocessing Steps
+Prior to training, we applied several preprocessing steps to standardize the dataset and enhance model performance:
+
+
+Augmentation: To increase the robustness of our model, we applied random rotations, flips, and color adjustments to simulate a variety of conditions.
+Normalization: Pixel values were normalized to aid in the model's faster convergence during training.
 
+## Training Procedure
+The model was trained using the following procedure:
 
-## Potential Applications
-- Manufacturing Quality Control: Streamline the process of identifying defective tires, ensuring that only the best-quality products reach the market.
-- Safety Compliance: Assist in meeting rigorous safety standards by detecting flaws that could compromise tire integrity.
-- Automotive Service Centers: Provide quick and reliable tire checks, enhancing customer service and safety.
-- Research and Development: Aid in the analysis of tire wear and degradation, contributing to the development of longer-lasting tire materials.
+Base Model: We used the YOLOv8s-cls pre-trained on the COCO dataset as our starting point.
+Fine-Tuning: The model was then fine-tuned on our specific tyre image dataset, indicating optimal learning.
+Batch Size: A particular batch size was employed to make the best use of our GPU memory resources.
+Optimizer: SGD optimizer was used for its fast convergence properties.
 
-## Getting Started
+
+## How to Use This Model
+This model is hosted on Hugging Face, making it incredibly easy to integrate and deploy. You can directly use our pre-trained model for classifying your tyre images by following the instructions in our usage documentation.
+
+### Getting Started
 
 ```bash
 pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
@@ -70,7 +87,6 @@ pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
 
 ```python
 from ultralyticsplus import YOLO, render_result
-import cv2
 
 # load model
 model = YOLO('foduucom/Tyre-Quality-Classification')
@@ -82,13 +98,44 @@ 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/document/images'
+image = '/path/to/your/document/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()
-```
+top_class_index = results[0].probs.top1
+Class = model.names[top_class_index]
+print(Class)
+
+```
+
+
+
+## Performance Metrics
+Our model achieved the following performance metrix on the test set:
+
+- Accuracy: 83.5%
+This metrix demonstrate the model's reliability in correctly classifying tyres as defective or good.
+Hardware and Software
+Training Environment: The model was trained on a machine with an NVIDIA RTX 3060 GPU, 32GB RAM, and an AMD Ryzen 5 processor.
+
+
+### Support and Contributions
+We are committed to the continuous improvement of our model and welcome contributions from the community. If you encounter any issues or would like to contribute, please refer to our contribution guidelines.
+
+### Stay Updated
+Don't forget to ⭐ this repository if you find this model useful! Follow this repo to stay updated with the latest enhancements and releases.
+
+Start harnessing the power of AI for tyre defect detection today – Download and integrate our fine-tuned YOLOv8s model now!
+
+
+```bibtex
+@ModelCard{
+  author    = {Nehul Agrawal, Rishabh Karnawat and
+               Rahul parihar},
+  title     = {Fine-Tuned YOLOv8s for Tyre Defect Detection},
+  year      = {2023}
+}
+```
+