Implementation and Evaluation of MobileNetV2 for Binary Image Classification on the Cats vs Dogs Dataset Using Transfer Learning
Keywords:
MobileNetV2, transfer learning, binary image classification, lightweight CNN, computer visionAbstract
This study presents the implementation and evaluation of a lightweight deep learning model based on MobileNetV2 for binary image classification of cats and dogs. Utilizing transfer learning with pretrained ImageNet weights, the model was fine-tuned on a balanced dataset of 24,998 labeled images sourced from Kaggle. The training pipeline incorporated data preprocessing and augmentation techniques, followed by two-phase training: feature extraction and fine-tuning. The final model achieved 98.9% validation accuracy, with precision, recall, and F1-score each reaching 0.99. The architecture, with only ~3.5 million trainable parameters and a file size of 14 MB, demonstrated fast inference (≈50 ms/image on GPU) and strong generalization. Despite its high performance, the model exhibited limitations under poor lighting, partial occlusion, and grayscale inputs. These findings confirm that MobileNetV2, when properly fine-tuned, offers an effective and efficient solution for real-time binary image classification tasks, and holds promise for future deployment in edge and mobile environments
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