Difference between revisions of "DSC180F20W7"
From Immersive Visualization Lab Wiki
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For this checkpoint you need to create a PyTorch application in Python to do the following things: | For this checkpoint you need to create a PyTorch application in Python to do the following things: | ||
| − | * [https://medium.com/howtoai/pytorch-torchvision-coco-dataset-b7f5e8cad82 | + | * Import the COCO dataset using the torchvision package. [https://medium.com/howtoai/pytorch-torchvision-coco-dataset-b7f5e8cad82 This tutorial may be useful.] |
* Teach a convolutional neural network image classification, using the COCO dataset. | * Teach a convolutional neural network image classification, using the COCO dataset. | ||
* Implement a demo for your image classification algorithm. | * Implement a demo for your image classification algorithm. | ||
Revision as of 22:08, 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. This tutorial may be useful.
- Teach a convolutional neural network image classification, using the COCO dataset.
- Implement a demo for your image classification algorithm.
Report Portion
- Describe the classification problem you set out to implement.
- Describe the approach of your solution.
- How accurate are the results of your image classification demo? (compare against ground truth)
- Suggest at least 3 things one could try to get more accurate results. (eg, network design, training data, training parameters, etc.)
