Enhance Machine Learning Model for industrial image anomoly detection
Budget: $30 – $250 USD
Hello,
I am working on a machine learning project intended for journal publicatio.I already have a working and novel model, and I will provide you with the existing code, current model flow, and reference diagrams. Your task is to improve and strengthen the model to meet strict research and publication standards.
This is purely research work, not a basic implementation task.
Core Model Requirements
You must clearly improve and redesign the following components:
• Multiscale feature extraction block (must be clearly defined and justified)
• Fusion block (current fusion is unclear and must be redesigned with a clear purpose)
• Memory bank
• Decoder / reconstruction module
The model must have clear, defensible novel contributions suitable for a research paper.
Research Expectations
• Read and analyze recent SOTA research papers on multiscale feature extraction, fusion techniques, and anomaly detection using deep learning and u need to use pytorch
• Identify limitations of existing SOTA models
• Improve or redesign components to address or outperform those limitations
• Clearly document:
• What is taken from prior work
• What is enhanced
• What is original contribution
• Provide a list of SOTA models used for comparison and explain why they were selected
Datasets
• MVTec Anomaly Detection (MVTec AD)
• My custom dataset (confidential)
• Training: 5 k plus normal images only
• Testing: very small dataset n two digit defectef images
Mandatory Research Protocols (Must Be Strictly Followed)
• No data leakage between training and testing
• Test dataset must be used only once for final evaluation
• Training must be done only on normal images (unsupervised learning)
• Synthetic images may be used only during training, not for testing
• Proper train/validation/test separation
• Fixed random seeds for reproducibility
• Fair and identical evaluation settings when comparing with SOTA models
• Proper ablation studies for each major component
• Follow standard SOTA research protocols used in top anomaly detection papers
Architecture, Diagrams & Explanation
• The complete model pipeline and architecture must be drawn using Draw.io
• Diagrams must be very clear, clean, and publication-ready
• i am particular about architecture diagrams
• You must:
• Explain the full model working step by step
• Explain the role of each component (multiscale block, fusion, memory bank, decoder, anomaly scoring)
• Explain data flow through the model
• Explain why each design choice was made
• Clearly explain how novelty is achieved
I must be able to fully understand and explain the model myself during professor meetings.
Training, Evaluation & Deliverables
• Train and evaluate on MVTec AD and my dataset
• Provide:
• Quantitative results and comparison tables
• Visual outputs (anomaly maps, reconstructions)
• Ablation study results
• Architecture diagram and flowchart
• A table listing:
• Each model component
• Source paper (if inspired)
• Enhancement made
• Original contribution
Data Access & Confidentiality
• My dataset is confidential
• Code must be executed only on my server
• Access will be provided via UltraViewer
• Data must not be copied, downloaded, or shared
Collaboration & Budget
•
• The model must be updated iteratively based on feedback until u achieve accuracy precision recall Auroc Auprc comaore to SOTA models
• This is not one time task
• Budget: USD $300, as the model is already developed and only requires improvement and refinement
I am working on a machine learning project intended for journal publicatio.I already have a working and novel model, and I will provide you with the existing code, current model flow, and reference diagrams. Your task is to improve and strengthen the model to meet strict research and publication standards.
This is purely research work, not a basic implementation task.
Core Model Requirements
You must clearly improve and redesign the following components:
• Multiscale feature extraction block (must be clearly defined and justified)
• Fusion block (current fusion is unclear and must be redesigned with a clear purpose)
• Memory bank
• Decoder / reconstruction module
The model must have clear, defensible novel contributions suitable for a research paper.
Research Expectations
• Read and analyze recent SOTA research papers on multiscale feature extraction, fusion techniques, and anomaly detection using deep learning and u need to use pytorch
• Identify limitations of existing SOTA models
• Improve or redesign components to address or outperform those limitations
• Clearly document:
• What is taken from prior work
• What is enhanced
• What is original contribution
• Provide a list of SOTA models used for comparison and explain why they were selected
Datasets
• MVTec Anomaly Detection (MVTec AD)
• My custom dataset (confidential)
• Training: 5 k plus normal images only
• Testing: very small dataset n two digit defectef images
Mandatory Research Protocols (Must Be Strictly Followed)
• No data leakage between training and testing
• Test dataset must be used only once for final evaluation
• Training must be done only on normal images (unsupervised learning)
• Synthetic images may be used only during training, not for testing
• Proper train/validation/test separation
• Fixed random seeds for reproducibility
• Fair and identical evaluation settings when comparing with SOTA models
• Proper ablation studies for each major component
• Follow standard SOTA research protocols used in top anomaly detection papers
Architecture, Diagrams & Explanation
• The complete model pipeline and architecture must be drawn using Draw.io
• Diagrams must be very clear, clean, and publication-ready
• i am particular about architecture diagrams
• You must:
• Explain the full model working step by step
• Explain the role of each component (multiscale block, fusion, memory bank, decoder, anomaly scoring)
• Explain data flow through the model
• Explain why each design choice was made
• Clearly explain how novelty is achieved
I must be able to fully understand and explain the model myself during professor meetings.
Training, Evaluation & Deliverables
• Train and evaluate on MVTec AD and my dataset
• Provide:
• Quantitative results and comparison tables
• Visual outputs (anomaly maps, reconstructions)
• Ablation study results
• Architecture diagram and flowchart
• A table listing:
• Each model component
• Source paper (if inspired)
• Enhancement made
• Original contribution
Data Access & Confidentiality
• My dataset is confidential
• Code must be executed only on my server
• Access will be provided via UltraViewer
• Data must not be copied, downloaded, or shared
Collaboration & Budget
•
• The model must be updated iteratively based on feedback until u achieve accuracy precision recall Auroc Auprc comaore to SOTA models
• This is not one time task
• Budget: USD $300, as the model is already developed and only requires improvement and refinement