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It is worth looking at the source code for the clustering, but keep in mind that it is implemented in OpenCV and clusters bounding boxes using a rectangle equivalence criterion that combines rectangles with similar sizes and locations.
Clusters with less than a set threshold of rectangles are rejected prior to a bounding box being generated.
For some applications you may want to change this or use a different methodology. A score for the final bounding boxes output is generated using a simplified mean Average Precision mAP calculation.
There is some debate about how to properly compute this, but we suggest you strive for consistency. For each predicted bounding box and ground truth bounding box the Intersection over Union IoU score is computed.
IoU is the ratio of the overlapping areas of two bounding boxes to the sum of their areas. According to the NVIDIA documentation, using a IoU threshold, predicted bounding boxes are designated as either true positive or false positive with respect to the ground truth bounding boxes.
If a ground truth bounding box cannot be paired with a predicted bounding box such that the IoU exceeds the threshold, then that bounding box is a false negative i.
In DIGITS, the simplified mAP score output is the product of the precision ratio of true positives to true positives plus false positives and recall ratio of true positives to true positives plus true negatives.
See Figure 3. The mAP is a metric for how sensitive the detection network is to objects of interest and how precise the bounding box estimates are.
We have use FCNs like the DetectNet to provide measurements in object tracking applications from video.
In these applications, it takes some patience to train the initial network using the filtered KITTI dataset. Understanding when to stop the training, save the weights and initialize a new training session using a custom dataset.
We have created many tools to enable the efficient generation of custom datasets from customer provided data or data we collect ourselves.
Although training and seeing the results from the FCN is a lot of fun, the bulk of the work is often in creating, formatting, and filtering custom datasets.
In addition to the benefits already mentioned, using an FCN is more efficient than using a CNN as a sliding window detector since it does not do any redundant calculations due to overlapping windows.
Using dual Titan X GPUs, we have trained detection networks for vehicle detection on images ranging from x to x pixels.
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A workaround for this is to write a custom training loop that performs the following:. I tried out the above-mentioned steps and my suggestion is not to go with the above strategy.
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We find the max height and width of images in a batch and pad every other image with zeros so that every image in the batch has an equal dimension.
The model automatically learns to ignore the zeros basically black pixels and learns features from the intended portion from the padded image.
This way we have a batch with equal image dimensions but every batch has a different shape due to difference in max height and width of images across batches.
You can run generator. One great addition to generator. The training script imports and instantiates the following classes:. The above objects are passed to the train function which compiles the model with Adam optimizer and categorical cross-entropy loss function.
We create a checkpoint callback which saves the best model during training. The best model is determined based on the value of loss calculated on the validation set at the end of each epoch.
I would suggest performing training on Google Colab unless you have a GPU in your local machine. The GitHub repo includes a Colab notebook which puts all the pieces together required for training.
You can modify the python scripts in Colab itself and train different model configurations on the dataset of your choice. Specify the path to the downloaded model.
This script uses the new features in TensorFlow 2. This SavedModel is required by TensorFlow serving docker image. To start TensorFlow Serving server, go to the directory where the SavedModel is exported.
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