Building a High-Accuracy Computer Vision Model for Flower Classification

Table of Contents

1. Introduction
2. Background and Education
3. The Data Science Boot Camp
4. Exploratory Data Analysis
5. Preparation for Modeling
6. Modeling Techniques
7. Training and Fine-Tuning
8. Model Achievements and Future Improvements
9. Conclusion
10. References

🌼 Article

Introduction

🌻 Welcome to this article! In this piece, we will delve into the intriguing world of computer vision and explore the author's experience with the Fellowship Challenge Problem involving the flower dataset. We will uncover the steps taken to build a computer vision model and provide insights into the data preprocessing, modeling techniques, and training approach. So, let's dive right in!

Background and Education

🌺 Allow me to introduce myself. My name is Maxwell Kman, and I am based in the vibrant San Francisco Bay Area. I hold a certificate of completion from Springboard's Data Science Career Track Boot Camp, and I am also a proud alumnus of the University of California Santa Cruz, where I obtained a Bachelor of Arts degree in pure mathematics.

The Data Science Boot Camp

🌺 During my time at the data science boot camp, I had the opportunity to work on a computer vision binary classification model that involved determining the edibility of mushrooms from images. This project fueled my excitement to embark on the Fellowship Challenge Problem, which focused on the flower dataset. Exploring the world of computer vision modeling techniques further enticed me, making me eager to join the Fellowship program and collaborate with like-minded machine learning enthusiasts while enhancing my portfolio.

Exploratory Data Analysis

🌺 To kickstart this captivating project, I started by importing the flowers dataset and the corresponding labels file in MATLAB. Upon inspection, I noticed that the labels were represented as integer values. To facilitate exploratory data analysis, I constructed a well-organized dataframe that mapped the integer labels to their respective English equivalents. This alignment immensely assisted in analyzing the dataset.

🌺 During the exploratory data analysis phase, I examined the distribution of classes, which revealed that the number of images per class varied from 40 to 258. Additionally, I delved into the pixel distribution across the red, green, and blue channels. Furthermore, I studied the distribution of image Dimensions and pixel intensities. Notably, I also observed significant variations in colors, textures, and backgrounds among flowers within the same class.

Preparation for Modeling

🌺 Aware of the need for data augmentation due to the aforementioned variations, I prepared the data for modeling. I divided the dataset into training, validation, and test sets. Taking into account the wide range of class distributions, I applied a weighting mechanism to address this issue. To facilitate this process, I leveraged the powerful fastai deep learning library.

🌺 Utilizing the capabilities of fastai, I created data loaders that employed a combination of normalization and augmentations. These carefully crafted transformations served as a crucial preparatory step before training the models. My approach followed a sequential methodology, starting with a base model loaded with default augmentations from the fastai library. Through an iterative process, I experimented with various combinations of augmentations and assessed their impact on model performance.

Modeling Techniques

🌺 Once the data was adequately prepared, I embarked on the modeling phase. To train the models, I employed the vision learner model from the fastai library, initialized with reset-50 architecture. I then tested each model's learning rate using the Learning Rate Finder technique. Employing the fit one cycle training method, I fine-tuned the models by employing transfer learning.

Training and Fine-Tuning

🌺 Armed with the invaluable knowledge gained through transfer learning, I achieved exceptional results. Remarkably, my best-performing model utilized resized images and a combination of transformations that were thoroughly explored during the experimentation phase. This model achieved an impressive accuracy score of 98.6%.

Model Achievements and Future Improvements

🌺 While thrilled with the remarkable performance of my model, I remain driven to enhance its capabilities. Some avenues for improvement include additional hyperparameter tuning, such as varying epoch counts, adjusting weight decay and variance, and experimenting with different batch sizes. Furthermore, I contemplate approaching the problem from a binary classification standpoint. Analyzing how each class performs against the rest of the dataset would enable me to identify the classes with the most challenging prediction results, allowing me to tailor augmentations to address these specific needs.

Conclusion

🌺 In conclusion, the Fellowship Challenge Problem involving the flower dataset provided an exciting opportunity to delve into computer vision modeling techniques. Through meticulous data preprocessing, thorough exploratory data analysis, and a sequential approach to modeling and training, I achieved impressive results. However, I am determined to continue refining my models and exploring avenues for improvement. This experience has fueled my passion for machine learning, and I am grateful for the opportunity to contribute to the field.

🌼 References

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Highlights

1. The author, Maxwell Kman, shares their experience with the Fellowship Challenge Problem involving the flower dataset.
2. This article uncovers the steps taken to build a computer vision model, including data preprocessing, modeling techniques, and training approaches.
3. Exploratory data analysis reveals interesting insights into the distribution of classes, pixel intensities, and variations in colors and textures among the flower images.
4. The author utilizes the fastai deep learning library for data preparation and augmentation.
5. Transferring learning techniques and fine-tuning are employed to enhance model performance.
6. The best-performing model achieves an accuracy score of 98.6%.
7. Future improvements include hyperparameter tuning and exploring binary classification approaches to address challenging prediction results.

FAQ

Q: What is the Fellowship Challenge Problem? A: The Fellowship Challenge Problem is a project that involves working with a specific dataset (in this case, the flower dataset) to build a computer vision model.

Q: What techniques were used for data augmentation? A: The author utilized the fastai deep learning library to apply normalization and various augmentations to the dataset.

Q: What was the best-performing model's accuracy score? A: The best-performing model achieved an impressive accuracy score of 98.6%.

Q: What are the future plans for improving the model? A: The author plans to explore additional hyperparameter tuning, such as varying epoch counts and adjusting weight decay and variance. They also intend to approach the problem as a binary classification task to address specific challenges within each class.

Q: What resources were used during the project? A: Relevant resources and references can be found at the end of the article.