A system and method of determining a neural network configuration may include receiving at least one neural network configuration, altering the received configuration for at least two iterations, calculating a first parameter of an altered configuration, calculating a second parameter of a consecutive altered configuration of the at least two iterations, comparing values of the calculated first parameter and second parameter, and determining a configuration having largest value of the calculated parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of determining a neural network configuration, the method comprising: receiving, by a processor, at least one neural network configuration; altering, by the processor, the received configuration for at least two iterations; calculating, by the processor, a first parameter of the altered configuration; calculating, by the processor, a second parameter of a consecutive altered configuration of the at least two iterations; comparing, by the processor, values of the calculated first parameter and second parameter; and determining, by the processor, a configuration having largest value of the calculated parameters. 2. The method as in claim 1, wherein the determined configuration defines a neural network to carry out an object detection algorithm. 3. The method as in claim 2, further comprising: receiving a set of labeled samples; performing evaluation of the received labeled samples with the object detection algorithm; and detecting an object from the set of labeled samples, with the object detection algorithm. 4. The method as in claim 2, wherein the configuration is determined to correspond with an object to be detected with the object detection algorithm. 5. The method as in claim 2, further comprising outputting the object detection algorithm. 6. The method as in claim 1, wherein said parameter is selected from the group consisting of a receiver operating characteristic curve, a confusing matrix, real-time performance, a true-positives rate and a false-positive rate. 7. The method as in claim 1, wherein said altering is carried out randomly. 8. The method as in claim 1, further comprising outputting the determined configuration. 9. The method as in claim 1, wherein said altering comprises altering at least one of the group consisting of a hidden layer, a number of neurons at a layer, number of filters at each layer, type of network, an activation function at a neuron, convolutional neural network, composition, fully connected neural network composition, pooling method at a layer, normalization method, stride, input layer size, and weight initializations. 10. The method as in claim 1, wherein at least one consecutive altered configuration of the at least two iterations is based on a configuration generated by a prior iteration, said configuration generated by said prior iteration having a largest value of said first parameter from prior iterations. 11. A system for determining a neural network configuration, comprising: a processor, configured to alter neural network configurations; a memory module, coupled to the processor; and a configuration database, coupled to the processor and configured to store neural network configurations; wherein the processor is further configured to calculate parameters for each alteration of neural network configurations stored on the configuration database. 12. The system as in claim 11, wherein the memory module is configured to store a received set of labeled samples. 13. The system as in claim 11, further comprising an object database coupled to the processor and configured to provide input of objects therefor. 14. The system as in claim 11, further comprising at least one detector coupled to the processor and configured to detect objects. 15. The system as in claim 14, wherein the alteration of neural network configurations corresponds to objects detected by the at least one detector. 16. A method of determining a neural network configuration, the method comprising: altering, by a processor, at least one neural network configuration; calculating, by the processor, a first parameter of the altered configuration; calculating, by the processor, a second parameter of different altered configuration; comparing, by the processor, values of the calculated first parameter and second parameter; and determining, by the processor, a configuration having largest value of the calculated parameters. 17. The method as in claim 16, wherein the determined configuration defines a neural network to carry out an object detection algorithm. 18. The method as in claim 17, further comprising: receiving a set of labeled samples; performing evaluation of the received labeled samples with the object detection algorithm; and detecting an object from the set of labeled samples, with the object detection algorithm. 19. The method as in claim 17, wherein the configuration is determined to correspond with an object to be detected with the object detection algorithm. 20. The method as in claim 16, wherein said altering is carried out randomly.