Inference is rapid, proceeding at 1000 square kilometers per hour on the NVIDIA T4 GPU available on the recently released (and free) Amazon SageMaker Studio Lab. configs/yoltv5_test_vehicles_8cat.yaml 4. To run inference, simply edit yoltv5_test_vehicles_8cat.yaml to point to the appropriate data locations, then run the test.sh script: cd yoltv5. Then simply run: cd yoltv5/ python yolov5/train.py -img 640 -batch 16 -epochs 100 -data yoltv5_train_vehicles_8cat.yaml -weights yolov5l.pt 3. Trainingĭata preparation for training is the same as in previous YOLT instances, see prep_train.py. So while the performance differences between YOLOv4/YOLOv5 (and hence YOLTv4/YOLTv5) are minimal, the movement from C to PyTorch can be quite impactful. For example, if one wants to experiment with AWS StudioLab, then the C libraries necessary for Darknet/YOLTv4 cannot currently be installed.
Yet there are times when a PyTorch backend is preferable to a C backend. There are speed advantages to the Darknet framework, as well as the advantage of using/supporting versions supported by the creator of YOLO. In the 5+ years since the original version of YOLT, we updated YOLT multiple times using YOLO versions based upon the original Darknet framework written in C. In this post we announce the release a version based upon the popular YOLOv5 framework: YOLTv5. A number of previous blogs covered YOLT/YOLT2, SIMRDWN (which includes YOLOv2, YOLOv3 and the TensorFlow object detection API as backends), and the most recent version based upon the original YOLO architecture: YOLTv4. We have actively tracked and leveraged this impressive framework for the YOLT overhead imagery detection models optimized for the enormous image sizes but tiny object sizes present in overhead imagery. The YOLO family of deep learning object detection models has enjoyed remarkable longevity, with new versions still under active development seven years after the original paper release.
Preface: This blog is part of a series describing the work done at Geodesic Labs.
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