Difference between revisions of "DSC180F20W7"
From Immersive Visualization Lab Wiki
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UNDER CONSTRUCTION | UNDER CONSTRUCTION | ||
| − | For Checkpoint 2 we are going to add deep learning to our toolkit, in order to eventually apply | + | For Checkpoint 2 we are going to add deep learning to our toolkit, in order to eventually apply the Grad-CAM XAI techniques to the COCO dataset (but that will not happen until the final checkpoint). |
| − | PyTorch is a machine learning API which builds on top of Torch. | + | PyTorch is a machine learning API which builds on top of Torch. Its most developed interface is in Python. |
| − | + | ==Code Portion== | |
| − | + | For this checkpoint you need to create a PyTorch application in Python to do the following things: | |
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| − | + | * [https://medium.com/howtoai/pytorch-torchvision-coco-dataset-b7f5e8cad82 Import the COCO dataset using the torchvision package.] | |
| − | + | * Teach a convolutional neural network image classification, using the COCO dataset. | |
| − | + | * Implement a demo for your image classification algorithm and analyze its results. | |
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Revision as of 22:03, 11 November 2020
Replication Checkpoint #2
UNDER CONSTRUCTION
For Checkpoint 2 we are going to add deep learning to our toolkit, in order to eventually apply the Grad-CAM XAI techniques to the COCO dataset (but that will not happen until the final checkpoint).
PyTorch is a machine learning API which builds on top of Torch. Its most developed interface is in Python.
Code Portion
For this checkpoint you need to create a PyTorch application in Python to do the following things:
- Import the COCO dataset using the torchvision package.
- Teach a convolutional neural network image classification, using the COCO dataset.
- Implement a demo for your image classification algorithm and analyze its results.
