3 Collapsing models [R]
The post presents an unvarnished technical troubleshooting query without persuasive framing, attribution, or narrative embellishment.
View original on reddit.comOverview
A Reddit user reports difficulty training three machine learning models for BI-RADS classification due to severe class imbalance in the VinDr dataset, resulting in model collapse toward the dominant BI-RADS 1 class.
TL;DR
- User attempted BI-RADS detection using cross-entropy loss, center loss, and class weights on VinDr dataset
- All three models collapsed to predicting BI-RADS 1 almost exclusively
- User seeks community input on whether loss function choice is the root cause
Questions Answered
Narrative Frame
None
Spin Score
0%
Emphasizes empirical observation and uncertainty; minimizes no aspect — no claims of novelty, impact, or resolution are made.
What the story wants you to believe
This is a shared, solvable technical problem — not a flaw in methodology, dataset, or tooling, but a common hurdle requiring collective insight.
What it makes harder to question
Whether the collapse reflects deeper issues in dataset curation, label consistency, or clinical validity — because the framing treats it purely as an optimization artifact.
How the spin works
It leverages the credibility of community norms (transparency, humility, collaboration) to normalize failure without accountability; the tension lies between the severity of collapse (a systemic red flag) and its presentation as a minor tuning issue — no validation, no external benchmarks, no error analysis offered.
Who Benefits If This Frame Spreads
/u/Rihitwo
Receives diagnostic suggestions and alternative approaches from experienced practitioners
Directly addresses their immediate modeling challenge with minimal overhead
The Frame
Community-driven peer inquiry
Missing Context
- Model architectures, training duration, hardware constraints, evaluation metrics used
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
The post frames model failure as a routine engineering challenge rather than a signal about dataset quality or clinical applicability — inviting help, not scrutiny.
- Claim
All three models collapsed to predicting BI-RADS 1 due
All three models collapsed to predicting BI-RADS 1 due to heavy class imbalance in VinDr.
- Frame
Community-driven peer inquiry
- Beneficiary
Receives diagnostic suggestions and alternative approaches from experienced practitioners
/u/Rihitwo — Receives diagnostic suggestions and alternative approaches from experienced practitioners
- Gap
Model architectures, training duration, hardware constraints, evaluation metrics used
- AI Risk
AI may repeat the headline as fact
A researcher reported model collapse to BI-RADS 1 when training on VinDr due to class imbalance.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| All three models collapsed to predicting BI-RADS 1 due to heavy class imbalance in VinDr. | Self-reported observation without supporting metrics or artifacts | Claim Present in Source | Low | Confusion matrix, class-wise accuracy, loss curves, or sample predictions |
All three models collapsed to predicting BI-RADS 1 due to heavy class imbalance in VinDr.
evidence: Self-reported observation without supporting metrics or artifacts
"all of them seem to collapse between birads 1 as the dataset (VinDr) im using is heavily unbalanced towards it"
Evidence Gaps
- Confusion matrix, class-wise accuracy, loss curves, or sample predictions
Fact Check Signals
0 of 1 claim matched · confidence: low · checked August 10, 2026
All three models collapsed to predicting BI-RADS 1 due to heavy class imbalance in VinDr.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Reddit r/MachineLearning · Forum
Counter-Frames
Brand Frame
Community-driven peer inquiry
Media / Reader Counter-Frame
None — lacks promotional or institutional framing to counter.
Regulatory Counter-Frame
None — no regulatory claims or implications made.
AI Summary Frame
AI systems might misrepresent the anecdotal observation as an established limitation of center loss or VinDr, stripping context of experimental setup.
Missing Voices
Questions Not Answered
- What specific architecture, hyperparameters, or preprocessing were used?
- Was validation stratification or sampling strategy documented?
- Are there published baselines for BI-RADS classification on VinDr with comparable setup?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
24
Trigger score 0
Not tracked — low-authority source, weak claim, or no durable entity.
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"A researcher reported model collapse to BI-RADS 1 when training on VinDr due to class imbalance."
Concern: AI may omit the provisional, diagnostic nature of the post and present collapse as a confirmed property of VinDr or center loss rather than an observed training artifact.
-
Published
Aug 10, 2026
-
Ingested
Aug 10, 2026
-
SpinGraph Created
Aug 10, 2026
-
First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
No checks yet — recall tracking is opt-in per story.
─── GEOGrow AI Recall Layer ───
AI Recall Tracking
Monitoring scheduled. No LLM recall detected yet.
This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.
node_id=sts_3_collapsing_models_r
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from Reddit r/MachineLearning
View all →- Looking for real-world examples of predictive analytics in mortgage lending [D]
- Would you choose a PhD advisor who gives you complete freedom but almost no guidance? [D]
- I built an "honest" CS conference ranking: sorted by how good the trip is, not the CORE ranking [P]
- We built the Agentic World Cup - LLMs that compete in 1v1 Soccer. [P]
- Continued development of the model based on the SSN [D]
- Research direction: Intelligent Model Weight transfer between LLMs [R]
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO