Vegetable Image Classification Project (CNN Model Training and Labeling)
ImageClassification
I designed and trained two Convolutional Neural Networks (CNNs) to classify vegetable images for a real-time identification system. The models were developed using Python and TensorFlow, leveraging MobileNet and ResNet 50 architectures. The classification work involved curating image datasets, processing and labeling them for supervised training of the AI models.
• Curated, cleaned, and labeled vegetable image datasets for model training.
• Applied supervised classification using annotated image data.
• Used TensorFlow and Python for model development and data processing.
• Ensured labeled data quality and model readiness for deployment.
I designed and trained two Convolutional Neural Networks (CNNs) to classify vegetable images for a real-time identification system. The models were developed using Python and TensorFlow, leveraging MobileNet and ResNet 50 architectures. The classification work involved curating image datasets, processing and labeling them for supervised training of the AI models. • Curated, cleaned, and labeled vegetable image datasets for model training. • Applied supervised classification using annotated image data. • Used TensorFlow and Python for model development and data processing. • Ensured labeled data quality and model readiness for deployment.
Not specified