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33 lines
1.3 KiB
Markdown
33 lines
1.3 KiB
Markdown
# Phase 1
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Getting started: PyTorch, Caltech101 dataset, ResNet50 and similarity measures
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- Refer phase1_project23.pdf for problem description
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- For task 3, the best distance measures seem to be:
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- Color moments - Pearson (faces especially)
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- Histogram of oriented gradients (HOG) - Cosine similarity
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- ResNet50 (avgpool, layer3, fc) - unsatisfactory results for all, simply used euclidean
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## Requirements and dependencies
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- Requires MongoDB server (local or otherwise)
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- Install packages from requirements.txt
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## Task 1 - task_1.ipynb
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After installing, run all cells in the notebook. There will be a prompt to give input for image ID. Range is 0 to 8677
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## Task 2 - task_2.ipynb
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Dataset processing and storage to database. Ensure MongoDB server is running, modify connection URI as needed if running on Atlas
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## Task 3 - task_3.ipynb
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Execute all cells till before the "Target images" markdown cell. There will be four prompts to give input for:
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- **Image ID**: integer, 0 to 8677
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- No. of similar images needed, **k**: positive integer
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- **Feature model** - one of ["cm", "hog", "avgpool", "layer3", "fc"]
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- _Note: only hog is applicable for all images. Others cannot be used for grayscale images_
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- **Similarity/distance measure** - one of ["euclidean", "cosine", "pearson"]
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