PhenoLeaf TS Models

--- license: apache-2.0 library_name: pytorch tags: - plant-phenotyping - instance-segmentation - image-classification - arabidopsis --- # PhenoLeaf-TS — Pretrained Models Baseline checkpoints for the [PhenoLeaf-TS](https://github.com/pisyntor/PhenoLeaf-TS) benchmark (ECCV 2026), trained on the [PhenoLeaf-TS dataset](https://huggingface.co/datasets/rick77a/PhenoLeaf_TS). | File | Model | Framework | Task | Score | |------|-------|-----------|------|-------| | `IS-3_mask_rcnn_r50.pth` | Mask R-CNN R50-FPN | Detectron2 | Leaf instance segmentation | **73.2 mAP** | | `CL-6_swin_t.pth` | Swin-T | timm | Growth-stage classification | **91.7 % acc** | | `IS-1_yolov11_seg.pt` | YOLOv11-seg | Ultralytics | Segmentation + tracking detector | 68.2 mAP | ## Usage ```python from phenoleaf_ts.models import load_model # see the GitHub repo seg = load_model("IS-3", checkpoint="IS-3_mask_rcnn_r50.pth") clf = load_model("CL-6", checkpoint="CL-6_swin_t.pth") # or pull a file directly from huggingface_hub import hf_hub_download ckpt = hf_hub_download("basimazam/PhenoLeaf-TS-models", "IS-1_yolov11_seg.pt") ``` ## Citation ```bibtex @InProceedings{saric2026phenoleafts, author = {Sari\'c, Rijad and Azam, Basim and Khan, Sarmad and \v{C}ustovi\'c, Edhem}, title = {{PhenoLeaf-TS}: A Time-Series Benchmark for Leaf Instance Segmentation, Tracking, and Growth Stage Classification}, booktitle = {Computer Vision -- ECCV 2026}, year = {2026}, publisher = {Springer Nature Switzerland}, } ```

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo basimazam/PhenoLeaf-TS-models