TPDD Honglin CLS Yolo26n Cls

--- license: other license_name: tla-derived-noncommercial license_link: https://github.com/honglin1226/Tobacco-Plant-Disease-Dataset library_name: ultralytics pipeline_tag: image-classification tags: - yolo - yolo26 - ultralytics - image-classification - tobacco - plant-disease - model-family datasets: - TamAko783/TPDD_Honglin_CLS metrics: - accuracy - f1 --- # yolo26n-cls — Tobacco Leaf Abnormality — **Model Family** A family of **YOLO26-nano classification** models (ImageNet-pretrained, fine-tuned) for tobacco leaf abnormality identification on the TLA dataset. Two members: | Member | Weights | Training data | |---|---|---| | **Base** | `base/yolo26n-cls-base.pt` | canonical real images + Tier-3 lesion-paste + colour-safe oversampling (`dataset_yolo_cls`) | | **Synthetic** | `synth/yolo26n-cls-synth.pt` | **Base, fine-tuned** on `combined_synth` = canonical + **762 SD-turbo diffusion rare-class variants** | Both classify all 16 TLA classes. **val/test are 100% real** and identical for both. > ## Dataset credit > Data collected & annotated by **Hong Lin, Rita Tse, Su-Kit Tang, Zhenping Qiang, > Giovanni Pau et al. (Macao Polytechnic University)** — TLA / TPDD. > Original repository: **<https://github.com/honglin1226/Tobacco-Plant-Disease-Dataset>**. > Cite TPDD (ICDIP 2022, [10.1117/12.2644288](https://doi.org/10.1117/12.2644288)) and > FREN (Frontiers in Plant Science 2024, [10.3389/fpls.2024.1333236](https://doi.org/10.3389/fpls.2024.1333236)). > Models trained by **TamAko783** (not the data creator). The source dataset carries > **no explicit license** → these models are released for **non-commercial research only**. ## Evaluation scope — COMMON classes only Under the leakage-safe 80/10/10 split, the four long-tail classes (`anthracnose`, `black_shank`, `tswv`, `genetic_abnormality`) have only **n=1** held-out image each, so a per-class score there is a coin-flip. **They are predicted by the models but NOT benchmarked.** All headline metrics below are over the **12 common classes** (≥8 test images): wildfire, brown_spot, frog_eye, target_spot, tmv, cmv, pvy, weather_fleck, sunscald, potato_tuber_moth, nematodes, healthy. ## Results (12 common classes, held-out real test = 120 images) | Metric | **Base** | **Synthetic** | |---|---|---| | Top-1 | 0.891 | **0.920** | | Macro-F1 (single split) | 0.884 | **0.900** | | **Macro-F1 (5-fold...