This project uses a ResNet50 deep learning model to classify flower images into five categories: daisy, dandelion, rose, sunflower, and tulip. Trained on the TensorFlow Flowers dataset, it includes a Streamlit web app for real-time image upload and prediction, providing accurate flower identification with a clean, interactive interface.

Flower Classification with CNNs and Transfer Learning: EfficientNet, ResNet50, and VGG16 Models

Flower Classification with Deep CNNs: A comparative study of ResNet50, DenseNet121, and MobileNetV3Small for classifying dahlia, daisy, rose, lily, and sunflower images using transfer learning in TensorFlow. Features custom data pipeline, performance analysis, and visualization tools for model evaluation.

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Fine-grained flower classification using ResNet50 & MobileNetV2 on the Oxford 102 dataset. Built with TensorFlow and deployed via Streamlit.

This project implements image classification using two different models: VGG16 and ResNet50. (Bu proje, VGG16 ve ResNet50 adlı iki farklı model kullanarak görüntü sınıflandırması yapmaktadır.) Both models are trained on the Flower Photos dataset. (Her iki model, Flower Photos veri seti üzerinde eğitilmiştir.)

Transfer learning project using ResNet50, VGG16, and MobileNetV2 on the Oxford Flowers 102 dataset. Includes data loading, preprocessing, model adaptation, training, evaluation, and comparison of results. Demonstrates the impact of transfer learning on fine-grained flower classification.

Fine-Grained-Image-Classification implements ResNet18 and ResNet50 models for fine-grained visual categorization on CUB-200 birds and Flowers-102 datasets, featuring focal loss, transfer learning, and detailed performance analysis.

• Developed a deep learning-based flower image classification model using Python and TensorFlow/Keras. • Utilized a pre-trained ResNet50 model for feature extraction and added custom layers for classification. • Applied transfer learning to improve model training efficiency and accuracy.

This project demonstrates the use of transfer learning to build an image classification model for five distinct classes of flowers. The model fine-tunes a pre-trained ResNet50 model using the PyTorch framework.

Flowers 102 Classification Using ResNet50

"ResNet50-based flower classification with data augmentation"

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Oxford Flowers 102 classification using ResNet50

I worked on a flower image classification project, where I utilized transfer learning with a pre-trained ResNet50 model. This project strengthened my skills in applying deep learning for image classification tasks, which directly aligns with the work done at Fellowship AI.

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Transfer learning-based image classifier for 102 flower categories using a fine-tuned ResNet50 model in PyTorch. Trained on the Oxford 102 Flowers dataset with two-phase training, OneCycleLR scheduling, and comprehensive evaluation tools.

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Worked with "102 Category Flower Dataset"

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This project implements a deep learning–based image classification system for recognizing different types of flowers using ResNet50 and PyTorch. The model is trained on a flower image dataset consisting of five classes and leverages transfer learning with ImageNet pre-trained weights.

Transfer Learning for Flower Image Classification using ResNet50

Flower classification using pretrained ResNet50 model and PyTorch.

Image classification of 102 flower types using ResNet50

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