Home > Storage > PowerScale (Isilon) > Industry Solutions and Verticals > Analytics > Dell Technologies Solution: Distributed Deep Learning Infrastructure for Autonomous Driving > ADAS Dataset and CNN Models
Since the entire Cityscapes raw data including label data is only 11 GB and can easily fit into system memory, the size of the dataset was increased 557 times to exercise the storage system (Isilon F800) properly We did this by applying 557 random data augmentation techniques to each JPEG image in the dataset. This is standard practice in data analytics and DL to increase size of data sets. In total, this “557x” dataset contained 1,657,075 images and annotations with 5TB in total. The average PNG image pixel is 1024 x 2048 and average size is 2,193KB. Figure 10 shows DataIQ scan results of the training dataset stored on the Isilon F800.
The performance test utilized in this document uses this data to train two different convolutional neural network (CNN) models, shown in Table 2 that are used for semantic image segmentation and object detection.
CNN Models | Purpose | Dataset | DL Framework | Distributed Framework |
DeepLabv3+ (backbone: ResNet-101) | Semantic Image Segmentation | Cityscapes Benchmark data with 1,657,075 training images and annotations (5TB in total) | PyTorch 1.4 | |
SSD: Single Shot MultiBox Detector (backbone: ResNet-50) | Real-Time Object Detection | Cityscapes Benchmark data with 972,825 training images (3TB in total) | PyTorch 1.4 | PyTorch Distributed Data Parallel |