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Recent work in neural architecture search has spawned interest in algorithms that can predict the performance of convolutional neural networks using minimum time and computation resources. We propose a new framework, Network Epoch Accuracy Prediction Framework (NEAP-F) which can predict the testing accuracy achieved by a convolutional neural network in one or more epochs. We introduce a novel approach to generate vector representations for networks, and encode ``ease" of classifying image datasets into a vector. For vector representations of networks, we focus on the layer parameters and connections between the network layers. A network achieves different accuracies on different image datasets; therefore, we use the image dataset characteristics to create a vector signifying the ``ease" of classifying the image dataset. After generating these vectors, the prediction models are trained with architectures having skip connections seen in current state-of-the-art architectures. The framework predicts accuracies in order of milliseconds, demonstrating its computational efficiency. It can be easily applied to neural architecture search methods to predict the performance of candidate networks and can work on unseen datasets as well.