Skip to content

Clarify README architecture #50

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
GuZZ1119 merged 1 commit into from
Jul 28, 2026
Merged

Clarify README architecture #50

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
58 changes: 46 additions & 12 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@
<a href="#delivered-work">Delivered Work</a> &bull;
<a href="#project-architecture">Architecture</a> &bull;
<a href="#evaluation-snapshot">Evaluation Snapshot</a> &bull;
<a href="#team-contributions">Team</a> &bull;
<a href="#references">References</a> &bull;
<a href="#reproducibility">Reproducibility</a>
</p>

Expand All @@ -30,9 +32,9 @@ This repository contains the completed COMP9517 group project for fine-grained i
| --- | --- |
| **Data and infrastructure** | Deterministic 500-class manifests, shared label metadata, Dataset/DataLoader utilities, manifest checks, and lightweight GitHub Actions validation. |
| **Traditional vision** | HOG, colour-histogram, and SIFT Bag-of-Visual-Words features; nearest-centroid, Linear SVM, and Random Forest classifiers; held-out evaluation and error analysis. |
| **Scratch CNN** | Randomly initialized ResNet18, training/validation loops, loss and Top-1 curves, checkpointing, guarded resume support, held-out evaluation, and error analysis. |
| **Scratch CNN** | Randomly initialized ResNet18, training/validation loops, loss and Top-1 curves, checkpointing, guarded resume support, held-out evaluation, error analysis, and Grad-CAM evidence. |
| **Pretrained CNN** | ImageNet-pretrained ResNet18 training, ablations, checkpoint recovery, held-out evaluation, and Grad-CAM evidence. |
| **Advanced study** | Fixed 100-class / 10-task continual-learning protocol, sequential no-replay and replay baselines, class-balanced sampling, task-accuracy matrices, forgetting metrics, and offline comparison figures. |
| **Advanced study** | Fixed 100-class / 10-task continual-learning protocol on both CNN routes, sequential no-replay and replay baselines, class-balanced sampling, task-accuracy matrices, forgetting metrics, and offline comparison figures. |

## Dataset Protocol

Expand All @@ -54,21 +56,35 @@ flowchart TB
Manifests --> DataLayer["Shared data layer<br/>config + Dataset + DataLoader"]
TaskMap["100-class continual task map"] --> DataLayer

DataLayer --> Traditional["Traditional vision<br/>HOG / colour / SIFT-BoVW<br/>SVM / Random Forest"]
DataLayer --> Scratch["Scratch CNN<br/>random ResNet18<br/>trainer + checkpoints"]
DataLayer --> Pretrained["Pretrained CNN<br/>ResNet18 training<br/>and ablations"]
Scratch --> CL["Continual learning<br/>no replay / replay<br/>task metrics"]
subgraph Baselines["500-class classification routes"]
direction LR
Traditional["Traditional vision<br/>HOG / colour / SIFT-BoVW<br/>SVM / Random Forest"]
Scratch["Scratch CNN<br/>random ResNet18<br/>trainer + checkpoints"]
Pretrained["Pretrained CNN<br/>ResNet18 training<br/>and ablations"]
end

Traditional --> Evaluation["Shared evaluation<br/>Top-1 / Top-5 / macro metrics<br/>predictions + confusion artifacts"]
Scratch --> Evaluation
Pretrained --> Evaluation
CL --> CLResults["Accuracy matrices<br/>forgetting curves<br/>offline summaries"]
Evaluation --> Explainability["Grad-CAM and error analysis"]
DataLayer --> Traditional
DataLayer --> Scratch
DataLayer --> Pretrained

Traditional --> TraditionalEval["Traditional evaluation<br/>metrics + confusion analysis"]
Scratch --> CNNEval["CNN evaluation<br/>metrics + predictions<br/>confusion artifacts"]
Pretrained --> CNNEval

Scratch --> ScratchCAM["Scratch Grad-CAM<br/>correct / incorrect<br/>confused-pair evidence"]
Pretrained --> PretrainedCAM["Pretrained Grad-CAM<br/>correct / incorrect<br/>confused-pair evidence"]

Scratch --> ScratchCL["Scratch continual learning<br/>no replay + replay"]
Pretrained --> PretrainedCL["Pretrained continual learning<br/>E04 comparison"]
ScratchCL --> CLResults["100-class task matrices<br/>old / seen accuracy<br/>average forgetting"]
PretrainedCL --> CLResults

CI["GitHub Actions<br/>syntax + manifest smoke checks"] --> Manifests
CI --> DataLayer
```

Grad-CAM is a CNN-only analysis: it is implemented for both the scratch and pretrained ResNet18 routes, not for the handcrafted traditional pipeline. The continual-learning study also has one branch for each CNN route; it uses the same 100-class task map and reports their task-boundary metrics separately.

## Evaluation Snapshot

All values below are from the completed configurations and their corresponding held-out test outputs unless otherwise stated.
Expand All @@ -90,7 +106,7 @@ The advanced experiment uses a deterministic 100-class subset split into 10 sequ
| Sequential fine-tuning without replay | 16.00% | 0.00% | 1.60% | 0.2600 |
| Replay, 5 images per old class, class-balanced sampling | 28.00% | 1.89% | 4.50% | 0.3322 |

This study is reported separately from the required 500-class baselines. Its saved artifacts include task-by-task accuracy matrices, current/old/seen accuracy curves, average-forgetting curves, replay-memory summaries, and final metric tables.
This study is reported separately from the required 500-class baselines. The held-out values above are from the scratch branch; the pretrained E04 branch uses the same task map and writes its own task matrices and comparison outputs. The saved artifacts include task-by-task accuracy matrices, current/old/seen accuracy curves, average-forgetting curves, replay-memory summaries, and final metric tables.

## Repository Layout

Expand All @@ -113,6 +129,24 @@ This study is reported separately from the required 500-class baselines. Its sav
`-- log.md # Project maintenance record
```

## Team Contributions

| Section | Member | Completed responsibilities |
| --- | --- | --- |
| A | Wang Haozhe | Data selection, preprocessing, and shared dataset manifests. |
| B | Liu Chaohao | Handcrafted feature extraction and traditional-method development. |
| C | Xu Hengyi | Classical classifiers, SIFT-BoVW experiments, and traditional-method evaluation. |
| D | Gu Xuanzhou | Scratch ResNet18, training/checkpoint infrastructure, scratch Grad-CAM, and continual-learning baselines. |
| E | Lin Guohao | Pretrained ResNet18, ablations, pretrained Grad-CAM, and extended continual-learning comparisons. |

## References

1. Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac Aodha. [Benchmarking Representation Learning for Natural World Image Collections](https://openaccess.thecvf.com/content/CVPR2021/html/Van_Horn_Benchmarking_Representation_Learning_for_Natural_World_Image_Collections_CVPR_2021_paper.html). CVPR, 2021.
2. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. [Deep Residual Learning for Image Recognition](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html). CVPR, 2016.
3. Ramprasaath R. Selvaraju et al. [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://doi.org/10.1109/ICCV.2017.74). ICCV, 2017.
4. Matthias De Lange et al. [A Continual Learning Survey: Defying Forgetting in Classification Tasks](https://doi.org/10.1109/TPAMI.2021.3057446). IEEE TPAMI, 2022.
5. Arslan Chaudhry et al. [On Tiny Episodic Memories in Continual Learning](https://arxiv.org/abs/1902.10486). arXiv:1902.10486, 2019.

## Reproducibility

Install the project dependencies and verify the committed data contract:
Expand Down
Loading