[BETTER] Download Yolov3

400 pixels should be fine.

I was going to say that retinanet might be a tad too slow for you on the nano. It uses a lot of CPU. Your laptop is probably i7 and it is much faster then nano.

training yolov3 is not hard but you have to transform your dataset to the coordinate system of yolo (if memory serves it is using centerpoint and (width,height) instead of the usual (x1, y1) (x2, y2))

not too difficult if you can script well.

Running my script using yolov3, in an Odroid N2 takes around 10 sec to analyze a frame, in the nano jetson, I was hoping for a positive surprise, it takes approx 4 sec. In an old retired laptop running debian, the same frame takes just below a second (In RPi, just forget)

Download Yolov3

Download File 🔥 https://urlgoal.com/2y7Y4c 🔥

Try _tflite_yolov3, but the NMS is not in the TensorFlow compute graph. You have to implement your own NMS in your JAVA code.Another issue with this repo is that it requires ONNX and PyTorch. If you are not familiar with them, it may cost you some time.

Load the SqueezeNet network pretrained on Imagenet data set and then specify the class names. You can also choose to load a different pretrained network trained on COCO data set such as tiny-yolov3-coco or darknet53-coco or Imagenet data set such as MobileNet-v2 or ResNet-18. YOLO v3 performs better and trains faster when you use a pretrained network.

Next, create the yolov3ObjectDetector object by adding the detection network source. Choosing the optimal detection network source requires trial and error, and you can use analyzeNetwork to find the names of potential detection network source within a network. For this example, use the fire9-concat and fire5-concat layers as DetectionNetworkSource.

The function modelGradients takes the yolov3ObjectDetector object, a mini-batch of input data XTrain with corresponding ground truth boxes YTrain, the specified penalty threshold as input arguments and returns the gradients of the loss with respect to the learnable parameters in yolov3ObjectDetector, the corresponding mini-batch loss information, and the state of the current batch.

Convert the predictions from the YOLO v3 grid cell coordinates to bounding box coordinates to allow easy comparison with the ground truth data by using the anchorBoxGenerator method of yolov3ObjectDetector.

Now what I have done is that I convert Darknet weight file of yolov3-tiny to onnx file, and I used tidl_model_import.out to complie onnx file to BIN file. But when I run tidl_model_import.out, it shows some error logs.

I am working on using yolov3 for end application. Since YOLOv3 is very huge I thought of using dynamic quantization. I have loaded the pre-trained weight and the model looks like below. Please help me with this problem.

Thank you for your reply. Unfortunally, object_detection_demo_yolov3_async also returns not reasonable results. However, in the last layer representing the detected bounding boxes, values between 0 and 1 are expected, representing the bounding box coordinates, the confidence and the class id. But the IR model also produces many negative values, values greater than 1 and NaNs.

gransanger19, converting of TF Yolo models with Model Optimizer is not so straightforward, and requires additional MO options, But likely whole process is well documented here -us/articles/OpenVINO-Using-TensorFlow#yolov3-to-ir

I want to find out, whether the problem is in my converted model or in my compilation of object_detection_demo_yolov3_async with Visual Studio 2017 (which produced some errors due to std::vectors delivered by a dll). It would be very nice, if you could take a look on my converted model and if you could check if you are able to parse it correctly (e.g. using object_detection_demo_yolov3_async) and to generate reasonable predictions on my test images.

The tinyyolov3Detect entry-point function takes an image input and runs the detector on the image. The function loads the network object from the tinyyolov3coco.mat file into a persistent variable yolov3Obj and reuses the persistent object during subsequent detection calls.

The yolov3_training_1000.weights file corresponds to the weights of first 1000 iterations, yolov3_training_final.weights file corresponds to the final weights generated after the training was completed and yolov3_training_last.weights file corresponds to the last saved weights just before the training was interrupted.

Next, the frozen_darknet_yolov3_model.pb is then converted into detect.tflite; a TensorFlow Lite version of the original model. This involves the use of TensorFlow 2.x.

At the end of cell execution, a file named as frozen_darknet_yolov3_model.pb would be generated in the tensorflow-yolo-v3 repository as shown below. This is the TensorFlow version of the original YOLOv3 model.

Alternatively, you can use Netron to visualize your model. To do that, download the frozen_darknet_yolov3_model.pb from the tensorflow-yolo-v3 repository (obtained at Step (v)) on your local machine. 006ab0faaa