https://github.com/yashbit/iscr_cnn

ISCR Implementations on CNN for Improving Orientation Detection in Models
https://github.com/yashbit/iscr_cnn

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ISCR Implementations on CNN for Improving Orientation Detection in Models

• Host: GitHub
• URL: https://github.com/yashbit/iscr_cnn
• Owner: YashBit
• License: mit
• Created: 2023-06-26T13:21:04.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-07-28T15:42:13.000Z (almost 2 years ago)
• Last Synced: 2025-02-23T04:16:36.930Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 38.4 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Project TODO List

## W&B Integration

- [ ] Set up W&B project for ISCR group: [ISCR W&B Project](https://wandb.ai/iscr)

## High-Level Overview

### Images

- **Compound/Continuous Set**

    - [ ] Prepare 2000 images for each letter

        - [ ] 1000 for continuous shapes

        - [ ] 1000 for compound shapes

    - **Total**: 52,000 images

### First Blocker

1. **How to Make Images Compound (Dotted Shapes)**

   - [ ] Gilles to provide dataset for compound letters

### Models

1. **Model Parameters**

    - [ ] Specify if ImageNet Pretrain is required (Y/N)

    - [ ] Define finetuning details (Y/N, dataset, parameters)

    - [ ] Specify mini datasets as needed for the image task

2. **Results**

    - [ ] Save model weights online

    - [ ] Integrate with Weights and Biases for comprehensive tracking

3. **Ablation Testing**

    - [ ] Script for ablating layers (parameters: layer number)

    - [ ] Measure performance metrics after ablating layers

4. **Build Charts**

    - [ ] Create performance charts similar to Paper 1

        - [ ] Human Similarity (Relative Score)

        - [ ] ...

## Experiment 1: Compound vs Continuous Letters

1. **Cognitive Science Concept Derived from Hypothesis**

    - [ ] Define hypothesis related to orientation of letters

    - [ ] Identify cognitive concept for experimentation

2. **Human Experimental Suite / Data**

    - [ ] Obtain experimental data from Davida (5.8/5.9)

3. **Computational Experiment**

    - [ ] Establish relation between DNNs and visual stream

    - [ ] Formulate assumptions related to cognitive concept

    - [ ] Prove cognitive concept using computational experiment

4. **Psychological Representations to Computational Experiment**

    - [ ] Establish connection between psychological and computational experiments

5. **Theory for Experiment**

    - [ ] Refine theory with Gilles

    - [ ] Formulate theories based on human capability and model training

6. **Experiment Details**

    - [ ] Train models on compound and continuous exemplars

    - [ ] Test models on compound/continuous exemplars

    - [ ] Conduct ablative analysis on model layers

## Engineering Methodology

1. **Models Other Than DNNs**

    - **Shallow Models:**

        - [ ] Pixelwise

        - [ ] GaborJet

        - [ ] Histogram of Oriented Gradient

        - [ ] Pyramid Histogram of Oriented Gradient

        - [ ] Pyramid Histogram of Visual Words

    - **HMAX Models:**

        - [ ] HMAX 99’

        - [ ] HMIN

        - [ ] HMAX-PNAS

2. **Deep Models**

    - **Models:**

        - [ ] GoogleLeNet

        - [ ] VGG

        - [ ] ResNet

    - **Image Set:**

        - [ ] Prepare 2000 images for each letter

            - [ ] 1000 for continuous shapes

            - [ ] 1000 for compound shapes

        - **Total**: 52,000 images

    - **Training Decisions:**

        - [ ] Pretrained on ImageNet + finetuned on use case

        - [ ] Fully trained only on use case, no ImageNet weights

3. **Evaluation**

    - [ ] Create bar charts of performance

    - [ ] Conduct ablative testing for each model and layer