Research map / Detection and segmentation / Open-set and few-shot detection
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use deep ResNet features instead of handcrafted SURF features with SVM | IP102 classification · IP102 test | accuracy: 19.5 -> 49.5 | Helped |
| Use ImageNet pre-training before fine-tuning ResNet | ResNet classification · IP102 test | accuracy: 35.7 -> 49.4 | Helped |
| Use FPN rather than YOLOv3 for detection | IP102 object detection · IP102 detection test | AP: 25.67 -> 28.10 | Helped |
| Evaluate ResNet on Mango rather than Rice sub-classes | ResNet classification · IP102 super-class subsets | accuracy: 32.1 -> 89.0 | Mixed |
Closest papers by meaning
- Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding (CVPR 2024 · similarity 0.91)
- Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring (NeurIPS 2025 · similarity 0.87)
- AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark (NeurIPS 2025 · similarity 0.86)
- BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity (NeurIPS 2024 · similarity 0.86)
- BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity (NeurIPS 2024 · similarity 0.85)
- CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale (ICLR 2025 · similarity 0.82)
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