Download Yolo.h5 Model __HOT_

YOLO works differently than most other object detection architectures. Most methods apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections. Yolo, on the other hand, applies a single neural network to the full image. The network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.

Now you should have a file called yolo.h5 in the model_data directory. This model can now be used for inference with the yolo_video.py file. Unfortunately, the yolo_video.py file in the repository contains a bug concerning the parsing of the argument. This was fixed by Github User Xiaokeai18, who opened a pull request fixing the error, which unfortunately wasn't accepted into the repository yet.

Download Yolo.h5 Model

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After entering the above commands, the model will load into memory, and you'll be able to specify the path to an image. After specifying the path, the model will try to detect all objects inside that image and show the result using matplotlib.

YOLO is one of the primary three types of object detectors you'll encounter. It works by dividing the image into regions and predicting bounding boxes and probabilities for each region. keras-yolo3 is a library that allows us to use and train YOLO models in Python with Keras.

I am using yad2k to convert the darknet YOLO model to a keras .h5 format. I have yolov3-voc.cfg, yolov3.weights, and yolov3.cfg all in the directory above the one that contains the yad2k script. When I run the following command:

You cannot convert YOLOv3 to Keras model using YAD2K. This is because YOLOv3's configuration file has a [shortcut] header. The yad2k.py file has no method to handle this header as it was written in the times of YOLOv2 (which doesn't have this layer/header).

I think you may have not downloaded the model correctly. Please clone the repository and then use the .h5 file. If you have downloaded from GIT, ensure you have downloaded raw data and no GIT tags are present in file. That might be the possible reason for "Invalid Network" error.

Hello. I have trained my Yolov3 model for object detection using Keras (with tensorflow backend). The trained model is saved using (model.save()) to an h5 file. I can correctly load the model and detect objects. Now, I would like to use this model on my RaspberryPi+Intel Movidius Neural Compute Stick. I have the OpenVINO toolkit installed and running properly on Raspberry pi.

[ ERROR ] Exception occurred during running replacer "TFYOLOV3" (): TensorFlow YOLO V3 conversion mechanism was enabled. Entry points "detector/yolo-v3/Reshape, detector/yolo-v3/Reshape_4, detector/yolo-v3/Reshape_8" were provided in the configuration file. Entry points are nodes that feed YOLO Region layers. Node with name detector/yolo-v3/Reshape doesn't exist in the graph. Refer to documentation about converting YOLO models for more information.

Thank you for the files provided. It seems that this yolov3 model looks different to the supported implementation of yolov3. Also, you are missing the --input_shape parameter, try using the following command and tell us if the issue persists:

I had tried with input_shape flag as well and it gave the same error. I assume the entry nodes are not the same as the ones model optimizer expect. the model is so large (its pbtxt) that I cannot open the text file and figure out these nodes myself either.

I think I am giving up on this issue. Instead, I tried a SSD detection model with Tensorflow object detection API which is supposedly more straightforward and standard to convert to IR. The conversion works with no errors, but I can't make any inference with the converted model. I made a new post about this new issue and would be grateful if you could please take a look at it.

Also, we could not find the equivalent entry points in your frozen model file. To check the nodes in your frozen model, you can use a model viewer tool, for example Netron, and visualize the model you created.

The Yolo model that the imageai library uses for object detection is available at the following Github Link. Download the yolo.h5 model from the above link. The next step is to import and load this model into your Python application.

This example loads a pretrained YOLOv5s model and passes an image for inference. YOLOv5 accepts URL, Filename, PIL, OpenCV, Numpy and PyTorch inputs, and returns detections in torch, pandas, and JSON output formats. See the YOLOv5 PyTorch Hub Tutorial for details.

There are three main variations of the approach, at the time of writing; they are YOLOv1, YOLOv2, and YOLOv3. The first version proposed the general architecture, whereas the second version refined the design and made use of predefined anchor boxes to improve bounding box proposal, and version three further refined the model architecture and training process.

Although the accuracy of the models is close but not as good as Region-Based Convolutional Neural Networks (R-CNNs), they are popular for object detection because of their detection speed, often demonstrated in real-time on video or with camera feed input.

It is a challenging model to implement from scratch, especially for beginners as it requires the development of many customized model elements for training and for prediction. For example, even using a pre-trained model directly requires sophisticated code to distill and interpret the predicted bounding boxes output by the model.

The YAD2K project was a de facto standard for YOLOv2 and provided scripts to convert the pre-trained weights into Keras format, use the pre-trained model to make predictions, and provided the code required to distill interpret the predicted bounding boxes. Many other third-party developers have used this code as a starting point and updated it to support YOLOv3.

Interestingly, experiencor has used the model as the basis for some experiments and trained versions of the YOLOv3 on standard object detection problems such as a kangaroo dataset, racoon dataset, red blood cell detection, and others. He has listed model performance, provided the model weights for download and provided YouTube videos of model behavior. For example:

Instead of using this program directly, we will reuse elements from this program and develop our own scripts to first prepare and save a Keras YOLOv3 model, and then load the model to make a prediction for a new photograph.

Next, we need to load the model weights. The model weights are stored in whatever format that was used by DarkNet. Rather than trying to decode the file manually, we can use the WeightReader class provided in the script.

At the end of the run, the model.h5 file is saved in your current working directory with approximately the same size as the original weight file (237MB), but ready to be loaded and used directly as a Keras model.

We can tie all of this together into a convenience function named load_image_pixels() that takes the filename and target size and returns the scaled pixel data ready to provide as input to the Keras model, as well as the original width and height of the image.

The output of the model is, in fact, encoded candidate bounding boxes from three different grid sizes, and the boxes are defined the context of anchor boxes, carefully chosen based on an analysis of the size of objects in the MSCOCO dataset.

The model has predicted a lot of candidate bounding boxes, and most of the boxes will be referring to the same objects. The list of bounding boxes can be filtered and those boxes that overlap and refer to the same object can be merged. We can define the amount of overlap as a configuration parameter, in this case, 50% or 0.5. This filtering of bounding box regions is generally referred to as non-maximal suppression and is a required post-processing step.

We also need a list of strings containing the class labels known to the model in the correct order used during training, specifically those class labels from the MSCOCO dataset. Thankfully, this is provided in the experiencor script.

@Jasin Brownlee, It was very interesting and very well narrated. Could you please include , how would we add additional training set and labels ?

Let say, I want this model to train on additional data, to classify Faces, or Hand Scribbled Digits and alphabets ? Suppose I have an additional training & test data set.

Let me ask about one more thing ? The output is a tensor of size n x S x S x (B*5 + C), where n is the number of anchor sizes, S x S is the number of cells, B is the number of anchors for each size and C is the number of classess. In our model we have 3 anchors in 3 different sizes, and the MSCOCO dataset has 80 classes, so the shape of the output tensor should be: (3,S,S,3*5+80), what is (3,S,S,95). However, our output is (3,S,S,255). Why is that?

I trained my own YOLOv3 model with custom classes (fewer than the default 80) using Darknet. I therefore have a new .cfg file as well as weights file.

I would like to perform inference using keras on Tensorflow, rather than Darknet.

Thank you so much for such a simple and elaborate explanation.

The code is working fine but I have a problem of not being able to save the model.

I get the following error when I save any model.

I meant that you have control about how the model will be used and what data will be provided to it. You control the environment and in turn you can ise this to limit the range/complexity when training the model.

Great explanation.

What if we have different set of classes? Can we train our new data-set on the same yolo model?

Also, how much time can it take to train if I have 10 different classes of object?

I want to run the trained YOLOv3 model on GPU for the purpose of object detection. Now, I can run your code (the code you explained above) on the CPU, but I do not know what I should do to run an object detection for an image on GPU. Can you please help me with this issue? I emphasize that I want to do it for just one image object detection, NOT for training the model. 006ab0faaa