Example outputs

[michalkrawczyk]

Short summary json from get_description_json(paper):

  {
    "Model Name": "ShapeCoder",
    "Model Category": "Shape modeling",
    "SOTA": false,
    "New Features": "Discovery of abstractions for visual programs from unstructured primitives",
    "Year": "2023",
    "filename": "2305.05661v1.ShapeCoder_Discovering_Abstractions_for_Visual_Programs_from_Unstructured_Primitives.pdf"
  }

Review obtained from YOLOv6 v3.0: A Full-Scale Reloading:
get_summary(paper)[-1] ( '-1' correspond response after last page analysis)

New Features:
- Bi-directional Concatenation (BiC) module for more accurate localization signals
- Anchor-aided training (AAT) strategy for the advantages of both anchor-based and anchor-free paradigms
- Extended backbone and neck design for better accuracy performance
- Self-distillation strategy to boost the performance of small models

New Strategies:
- Bi-directional FPN for hierarchical feature representation
- Anchor-aided training (AAT) for improved performance
- Self-distillation strategy for boosting small model performance

Problems:
- None explicitly stated

Design:
- Enhanced-PAN as detection neck with Bi-directional Concatenation (BiC) module
- Simplified SPPF block to SimCSPSPPF Block
- RepBi-PAN framework for neck of YOLOv6

Results:
- "YOLOv6-N has significantly advanced by 9.5%/4.2% respectively compared to YOLOv5-N/YOLOv7-Tiny (input size=416)."
- "YOLOv6-S can improve AP by 3.5%/0.9% with higher speed compared to YOLOX-S/PPYOLOE-S."
- "YOLOv6-M outperforms YOLOv5-M by 4.6% higher AP with a similar speed, and it achieves 3.1%/1.0% higher AP than YOLOX-M/PPYOLOE-M at a higher speed."
- "YOLOv6-L is 3.1%/1.4% more accurate than YOLOX-L/PPYOLOE-L under the same latency constraint."
- "YOLOv6-N hits 37.5% AP on COCO dataset at a throughput of 1187 FPS."
- "YOLOv6-S strikes 45.0% AP at 484 FPS, outperforming other mainstream detectors at the same scale."
- "YOLOv6-M/L achieve better accuracy performance (50.0%/52.8% respectively) than other detectors at a similar inference speed."
- "YOLOv6-L6 achieves state-of-the-art accuracy in real-time."