PHBench vs Qwen-Image-Layered: Features, Pricing & Which Is Better (2026)

Key features

• Large-Scale Mapping: Links 67,292 featured Product Hunt posts to 528 verified Series A outcomes within an 18-month horizon, enabling longitudinal outcome prediction.
• Engineered Signal Set: Provides 61 engineered features per post including engagement signals (votes, comments, reviews), rank signals (daily/weekly/monthly), maker features (maker count, followers), temporal features, topic flags, and interaction terms to support rich modeling.
• Structured Splits and Imbalanced Labels: Published train/validation/test splits (Train: 47,071; Val: 6,753; Test: 13,468) with measured positive rates (~0.76–0.79%), plus withheld test labels for blind benchmark evaluation.
• Evaluation & Submission Workflow: Test labels are withheld and researchers submit predictions (email to benchmark@vela.partners) for centralized scoring to enable fair comparison between models.
• Open License & Citation: Distributed under CC BY 4.0 (per Hugging Face dataset page) with a required citation (Ihlamur et al., PHBench arXiv 2026) for academic and research use.
• Supporting Code & Graph Tools: Associated code and GNN/graph-analysis workflows are available (Weave project on GitHub) to build graph representations and run node-classification experiments; dataset access may require contacting Vela Partners due to access conditions.
• Mapped dataset of 67,292 Product Hunt featured posts linked to 528 verified Series A outcomes (18-month horizon, 2019–2025).
• 61 engineered features per post: engagement signals (votes, comments, reviews), rank signals (daily, weekly, monthly), maker features (maker count, followers), temporal features, topic flags, and interaction terms.
• Standard train/validation/test splits with class imbalance details (Train: 47,071 posts, 372 positives; Val: 6,753 posts, 53 positives; Test: 13,468 posts, test labels withheld).
• Withheld test labels and centralized scoring: submit predictions to benchmark@vela.partners for evaluation.
• Hosted on Hugging Face Datasets with CC-BY-4.0 license; access requires agreeing to share contact information.
• Suitable for benchmarking binary classification models, feature-ablation studies, imbalanced learning experiments, and startup outcome research.
• Tabular data format compatible with common ML tooling (Hugging Face Datasets, pandas, scikit-learn, PyTorch, TensorFlow).
• Includes citation: Ihlamur et al., "PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals", arXiv 2026.

Best for

• Early-Stage Deal Prioritization: Train classifiers to rank Product Hunt launches by probability of raising Series A within 18 months to help investors triage and prioritize founder outreach.
• Research on Launch Signals: Analyze which launch-day signals (engagement, rank, maker attributes) most strongly correlate with later funding to inform product and marketing strategies.
• Benchmarking Models: Use the withheld-test benchmark to compare classical ML, deep learning, and LLM-based approaches for startup outcome prediction under standardized splits.
• Feature Engineering Studies: Develop and validate new derived signals or temporal interaction features using PHBench’s engineered feature set to improve predictive performance.
• Graph & GNN Experiments: Construct graph representations of makers, posts, and interactions (using the Weave tooling) to evaluate graph neural networks for node-level fundraising prediction.
• Tooling for Founders: Build launch-advising tools that estimate fundraising likelihood from Product Hunt metrics and suggest actions to improve discovery and traction.
• Benchmarking binary classifiers for predicting Series A funding from early launch signals.
• Feature engineering and ablation studies on engagement, rank and maker features.
• Research on imbalanced classification methods and calibration for rare events.
• Startup scouting and signal analysis for VC or accelerator decision support.
• Time-window outcome modeling and survival/time-to-event approximations using launch temporal features.

Key features

• Layered image composition and analysis
• Multimodal inputs (text + image)
• Model weights and code published on GitHub
• Self-hosting and fine-tuning capability
• Playable via cloud-hosted inference when provided by Alibaba Cloud
• Public GitHub repository for the Qwen3 model series (source link provided)
• Developed and maintained by the Qwen team at Alibaba Cloud
• Repository-level hosting of model assets, documentation, and code for the Qwen3 series
• No specific feature list for 'Qwen-Image-Layered' is present in the provided content
• Technical APIs, integrations, platforms, and requirements are not detailed in the provided content

Best for