Pseudo Nitzschia Yolo11s Seg

--- license: mit tags: - yolo - ultralytics - instance-segmentation - object-detection - phytoplankton - pseudo-nitzschia - ifcb - cell-counting - marine-biology pipeline_tag: image-segmentation datasets: - patcdaniel/pseudo-nitzschia-cell-segmentation --- # Pseudo-nitzschia Cell Counter — YOLO11s-seg A YOLO11s instance segmentation model fine-tuned to detect and count individual *Pseudo-nitzschia* cells within chain colonies in Imaging FlowCytobot (IFCB) images. **Try it out:** [Interactive demo on HF Spaces](https://huggingface.co/spaces/patcdaniel/pseudo-nitzschia-counter) ## Performance | Metric | Box | Mask | |--------|-----|------| | Precision | 0.983 | 0.983 | | Recall | 0.965 | 0.965 | | mAP50 | 0.976 | 0.973 | | mAP50-95 | 0.706 | 0.659 | Trained for 100 epochs on Apple MPS (M-series GPU). Training time: ~1.5 hours. ### Training Curves  ### Confusion Matrix  ## Quick Start ```python from ultralytics import YOLO from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download("patcdaniel/pseudo-nitzschia-yolo11s-seg", "best.pt") model = YOLO(model_path) # Run inference results = model("image.png", conf=0.25) # Count cells for r in results: n_cells = len(r.masks) if r.masks is not None else 0 print(f"Detected {n_cells} cells") ``` ## Training ### 1. Download the dataset ```bash huggingface-cli download patcdaniel/pseudo-nitzschia-cell-segmentation \ --repo-type dataset --local-dir ./dataset ``` ### 2. Train ```bash python scripts/train_yolo.py \ --config configs/cell_seg.yaml \ --model yolo11s-seg.pt \ --epochs 100 \ --batch 24 \ --name pn_cell_seg_yolo11s ``` ### 3. Evaluate ```bash python scripts/inference.py \ --weights runs/segment/pn_cell_seg_yolo11s/weights/best.pt \ --source dataset/data/images/ \ --conf 0.25 \ --save_viz ``` ## Model Details - **Architecture:** YOLO11s-seg (Ultralytics) - **Base weights:** `yolo11s-seg.pt` (COCO pre-trained) - **Input size:** 640x640 - **Classes:** 1 (`Pseudo_nitzschia_cell`) - **Parameters:** ~11.8M - **Model size:** 20 MB ### Training Hyperparameters | Parameter | Value | |-----------|-------| | Epochs | 100 | | Batch size | 24 | | Image size | 640 | | Optimizer | auto (SGD) | | Initial LR | 0.01 | | Final LR factor | 0.01 | | Weight decay | 0.00...