This paper introduces Fast R-CNN, a method for object detection that improves upon previous approaches by enhancing training and testing speed while increasing detection accuracy. Fast R-CNN builds on R-CNN and SPPnet, offering faster training (9× faster than R-CNN, 3× faster than SPPnet) and testing (213× faster than R-CNN) with higher mAP on PASCAL VOC 2012. It is implemented in Python and C++ using Caffe and is available under the MIT License. Fast R-CNN uses a single-stage training algorithm that jointly learns to classify object proposals and refine their spatial locations. It processes images efficiently, achieving top accuracy on PASCAL VOC 2012 with a mAP of 66% (vs. 62% for R-CNN). The method includes a RoI pooling layer that extracts fixed-length feature vectors from feature maps, enabling efficient processing of object proposals. Fast R-CNN also introduces a multi-task loss that jointly trains for classification and bounding-box regression, improving accuracy and efficiency. It uses truncated SVD to accelerate detection by compressing fully connected layers, reducing detection time with minimal impact on mAP. The method supports scale invariance through single-scale or multi-scale training, with single-scale offering the best speed/accuracy tradeoff. Experiments show that Fast R-CNN achieves state-of-the-art mAP on VOC07, 2010, and 2012, with faster training and testing compared to R-CNN and SPPnet. It also demonstrates that fine-tuning convolutional layers in VGG16 improves mAP. The method is efficient, requiring no disk storage for feature caching and enabling direct evaluation of object proposal mAP. Fast R-CNN outperforms previous methods in detection accuracy and efficiency, with results showing that multi-task training improves classification accuracy, single-scale processing offers the best speed/accuracy tradeoff, and more training data improves mAP. The method also shows that using dense boxes can be less effective than sparse proposals, and that SVMs with hard negative mining are not necessary for dense box detection. Fast R-CNN is applied to the MS COCO dataset, achieving a preliminary baseline with a PASCAL-style mAP of 35.9% and a COCO-style AP of 19.7%.This paper introduces Fast R-CNN, a method for object detection that improves upon previous approaches by enhancing training and testing speed while increasing detection accuracy. Fast R-CNN builds on R-CNN and SPPnet, offering faster training (9× faster than R-CNN, 3× faster than SPPnet) and testing (213× faster than R-CNN) with higher mAP on PASCAL VOC 2012. It is implemented in Python and C++ using Caffe and is available under the MIT License. Fast R-CNN uses a single-stage training algorithm that jointly learns to classify object proposals and refine their spatial locations. It processes images efficiently, achieving top accuracy on PASCAL VOC 2012 with a mAP of 66% (vs. 62% for R-CNN). The method includes a RoI pooling layer that extracts fixed-length feature vectors from feature maps, enabling efficient processing of object proposals. Fast R-CNN also introduces a multi-task loss that jointly trains for classification and bounding-box regression, improving accuracy and efficiency. It uses truncated SVD to accelerate detection by compressing fully connected layers, reducing detection time with minimal impact on mAP. The method supports scale invariance through single-scale or multi-scale training, with single-scale offering the best speed/accuracy tradeoff. Experiments show that Fast R-CNN achieves state-of-the-art mAP on VOC07, 2010, and 2012, with faster training and testing compared to R-CNN and SPPnet. It also demonstrates that fine-tuning convolutional layers in VGG16 improves mAP. The method is efficient, requiring no disk storage for feature caching and enabling direct evaluation of object proposal mAP. Fast R-CNN outperforms previous methods in detection accuracy and efficiency, with results showing that multi-task training improves classification accuracy, single-scale processing offers the best speed/accuracy tradeoff, and more training data improves mAP. The method also shows that using dense boxes can be less effective than sparse proposals, and that SVMs with hard negative mining are not necessary for dense box detection. Fast R-CNN is applied to the MS COCO dataset, achieving a preliminary baseline with a PASCAL-style mAP of 35.9% and a COCO-style AP of 19.7%.