This paper introduces a context-free network (CFN), a convolutional neural network (CNN) trained to solve jigsaw puzzles as a pretext task, which enables the learning of visual representations that can be transferred to object classification and detection tasks. The CFN is designed to limit the receptive field of its early processing units to one tile at a time, allowing it to learn both the features of object parts and their spatial arrangement. The CFN has fewer parameters than AlexNet while maintaining similar semantic learning capabilities. The paper shows that the learned features capture semantically relevant content and outperform state-of-the-art methods in several transfer learning benchmarks. The CFN is trained to solve jigsaw puzzles by predicting the correct permutation of tiles. The network is trained on a large dataset of images, with each image split into a 3x3 grid of tiles. The CFN is then used to solve the jigsaw puzzle task by predicting the correct permutation of the tiles. The network is trained to learn features that are useful for both the jigsaw puzzle task and for object classification and detection tasks. The CFN is evaluated on several tasks, including object classification, object detection, and semantic segmentation. The results show that the CFN outperforms other methods in these tasks. The paper also performs ablation studies to evaluate the impact of different components of the CFN on its performance. The results show that the CFN is effective in learning visual representations that are useful for both the jigsaw puzzle task and for object classification and detection tasks. The CFN is trained using a self-supervised learning approach, where the jigsaw puzzle task serves as a pretext task. The network is trained to learn features that are useful for both the jigsaw puzzle task and for object classification and detection tasks. The CFN is evaluated on several tasks, including object classification, object detection, and semantic segmentation. The results show that the CFN outperforms other methods in these tasks. The paper also performs ablation studies to evaluate the impact of different components of the CFN on its performance. The results show that the CFN is effective in learning visual representations that are useful for both the jigsaw puzzle task and for object classification and detection tasks.This paper introduces a context-free network (CFN), a convolutional neural network (CNN) trained to solve jigsaw puzzles as a pretext task, which enables the learning of visual representations that can be transferred to object classification and detection tasks. The CFN is designed to limit the receptive field of its early processing units to one tile at a time, allowing it to learn both the features of object parts and their spatial arrangement. The CFN has fewer parameters than AlexNet while maintaining similar semantic learning capabilities. The paper shows that the learned features capture semantically relevant content and outperform state-of-the-art methods in several transfer learning benchmarks. The CFN is trained to solve jigsaw puzzles by predicting the correct permutation of tiles. The network is trained on a large dataset of images, with each image split into a 3x3 grid of tiles. The CFN is then used to solve the jigsaw puzzle task by predicting the correct permutation of the tiles. The network is trained to learn features that are useful for both the jigsaw puzzle task and for object classification and detection tasks. The CFN is evaluated on several tasks, including object classification, object detection, and semantic segmentation. The results show that the CFN outperforms other methods in these tasks. The paper also performs ablation studies to evaluate the impact of different components of the CFN on its performance. The results show that the CFN is effective in learning visual representations that are useful for both the jigsaw puzzle task and for object classification and detection tasks. The CFN is trained using a self-supervised learning approach, where the jigsaw puzzle task serves as a pretext task. The network is trained to learn features that are useful for both the jigsaw puzzle task and for object classification and detection tasks. The CFN is evaluated on several tasks, including object classification, object detection, and semantic segmentation. The results show that the CFN outperforms other methods in these tasks. The paper also performs ablation studies to evaluate the impact of different components of the CFN on its performance. The results show that the CFN is effective in learning visual representations that are useful for both the jigsaw puzzle task and for object classification and detection tasks.