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14-332-472-01-ROBOTICS-COMP-VISION-Classify-ImageNet-classes-with-ResNet50-
14-332-472-01-ROBOTICS-COMP-VISION-Classify-ImageNet-classes-with-ResNet50- PublicClassifying ImageNet classes with ResNet50 Using pytorch by setting up the pre-trained network. Start by obtaining 10 images that are similar to Imagenet classes and classifying them. Then choose 1…
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real-time-audio-fpga
real-time-audio-fpga PublicReal-time audio processing and transmission system on a Xilinx Zynq FPGA using Verilog, C, and Python.
SystemVerilog 1
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14-332-472-01-ROBOTICS-COMP-VISION-Classify-MNIST-classes-with-ResNet18
14-332-472-01-ROBOTICS-COMP-VISION-Classify-MNIST-classes-with-ResNet18 PublicClassify MNIST classes with ResNet18 Fine-tune the ResNet 18 network to classify the MNIST dataset. Report the confusion matrix, the accuracy, the f-score, precision and recall of your classifier. …
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14-332-472-01-ROBOTICS-COMP-VISION-Classify-Dog-vs-Cat-Kaggle-dataset-with-two-different-networks
14-332-472-01-ROBOTICS-COMP-VISION-Classify-Dog-vs-Cat-Kaggle-dataset-with-two-different-networks PublicClassify Dog vs Cat Kaggle dataset with two different networks Fine-tune a pre-trained network for the dog vs. cat classification problem. Report the confusion matrix, the accuracy, the f-score, pr…
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14-332-472-01-ROBOTICS-COMP-VISION-adversarial-attack
14-332-472-01-ROBOTICS-COMP-VISION-adversarial-attack PublicFind and perform an adversarial attack (such as adding noise) that will make the 10 images from the first question difficult to recognize.
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