what is best YOLO model for microbiology like cell...

For microbiology cell segmentation on RTX 2060 / RTX 3060, I’d choose:

Best practical YOLO choice

Use yolo26s-seg.pt as your default starting model.

Why: it is small enough for RTX 2060/3060, but much better than Nano for masks. Ultralytics lists YOLO26 segment models as the current segmentation family, with yolo26s-seg at 10.4M params, 40.0 mask mAP, and fast TensorRT inference; yolo26m-seg improves mask mAP to 44.1 but is much heavier. (Ultralytics Docs)

My GPU-based recommendation

GPU	Recommended model	Notes
RTX 2060 6GB	yolo26n-seg.pt or yolo26s-seg.pt	Start with s; use n if you get CUDA out-of-memory.
RTX 3060 laptop 6GB	yolo26s-seg.pt	Similar to RTX 2060 VRAM limits.
RTX 3060 desktop 12GB	yolo26s-seg.pt → yolo26m-seg.pt	Start with s; try m if accuracy matters more than speed.
Tiny cells / nuclei	yolo26s-seg.pt or yolo26m-seg.pt + tiling	Higher image size or tiled training matters more than using a huge model.

Accuracy vs speed ranking

For your hardware:

1. Best balance: yolo26s-seg.pt
2. Best for 6GB VRAM: yolo26n-seg.pt
3. Best accuracy on RTX 3060 12GB: yolo26m-seg.pt
4. Avoid unless you have more VRAM: yolo26l-seg.pt, yolo26x-seg.pt

YOLO26 is the latest Ultralytics YOLO line and is designed for edge / low-power deployment with NMS-free inference and segmentation-specific improvements. Ultralytics also says both YOLO26 and YOLO11 are recommended for stable production workloads. (Ultralytics Docs)

If you want a safer, older/stable choice

Use yolo11s-seg.pt or yolo11m-seg.pt.

YOLO11 segmentation is still very good: yolo11s-seg has 10.1M params and 37.8 mask mAP, while yolo11m-seg has 22.4M params and 41.5 mask mAP. (Ultralytics Docs) It is generally a better modern choice than YOLOv8; YOLOv8s-seg has 36.8 mask mAP, and YOLOv8m-seg has 40.8 mask mAP. (Ultralytics Docs)

Important for microbiology/cells

For cells, the model size is not the only thing. The biggest factors are:

Use instance segmentation, not semantic segmentation, if you need each cell separated.

Use high image size or tiling. Cells are often small; training at imgsz=640 may miss fine boundaries. Try imgsz=768, 1024, or tile large microscope images into patches.

Annotate masks carefully. YOLO segmentation needs polygon masks. Bad boundaries = bad cell masks.

For touching/overlapping cells, YOLO can struggle compared with biology-specific tools. You should benchmark against Cellpose and StarDist. Cellpose is explicitly designed as a generalist cellular segmentation method, while StarDist is designed for nuclei/cell segmentation in 2D/3D microscopy, especially star-convex nuclei-like shapes. (Springer Nature Experiments)

Suggested training command

bash
yolo segment train \
  model=yolo26s-seg.pt \
  data=cell_dataset.yaml \
  imgsz=768 \
  epochs=150 \
  batch=4 \
  device=0 \
  patience=40 \
  amp=True

For RTX 2060 6GB, reduce if needed:

bash
yolo segment train model=yolo26s-seg.pt data=cell_dataset.yaml imgsz=640 batch=2 device=0 amp=True

For RTX 3060 12GB, try:

bash
yolo segment train model=yolo26m-seg.pt data=cell_dataset.yaml imgsz=768 batch=4 device=0 amp=True

Final recommendation

For your case, I would start with:

text
yolo26s-seg.pt

Then compare against:

text
yolo26n-seg.pt   # faster / less VRAM
yolo26m-seg.pt   # better accuracy if RTX 3060 12GB can handle it
yolo11s-seg.pt   # stable fallback

For pure microscopy accuracy, also test Cellpose or StarDist as a baseline; for real-time deployment and easy YOLO workflow, use YOLO26s-seg.

Use this version first. It keeps YOLO11s segmentation, uses your 900 train / 100 val split, does no test, and is tuned for 1280×720 blood-cell masks.

python
from ultralytics import YOLO

# Good safe model for RTX 2060 / RTX 3060
model = YOLO("yolo11s-seg.pt")

results = model.train(
    data="custom_dataset.yaml",

    # For 1280x720 blood-cell images, 640 is usually too low.
    # 960 is a good balance for RTX 2060/3060.
    imgsz=960,

    # Auto-selects batch based on GPU memory.
    # Good for switching between RTX 2060 6GB and RTX 3060 12GB.
    batch=-1,

    epochs=200,
    patience=50,

    device=0,
    workers=0,        # keep 0 if you are on Windows; use 4 or 8 on Linux if stable

    # Training behavior
    pretrained=True,
    optimizer="AdamW",
    lr0=0.001,
    lrf=0.01,
    weight_decay=0.0005,
    cos_lr=True,
    amp=True,
    seed=42,

    # Segmentation-specific settings
    mask_ratio=2,       # better mask detail than default 4
    overlap_mask=False, # better for separated/touching cell instances; use True if VRAM problem
    max_det=1000,       # blood-cell images can contain many cells

    # Validation during training only; this is NOT a test run
    val=True,
    plots=True,
    save=True,

    # Augmentation tuned for microscopy/blood cells
    degrees=180,
    flipud=0.5,
    fliplr=0.5,
    translate=0.05,
    scale=0.35,
    shear=0.0,
    perspective=0.0,

    # Keep color augmentation moderate because stain/color can matter
    hsv_h=0.015,
    hsv_s=0.30,
    hsv_v=0.20,

    # Mosaic/copy-paste: useful, but not too strong for microscopy
    mosaic=0.25,
    close_mosaic=30,
    mixup=0.0,
    copy_paste=0.10,

    project="runs/blood_cell_seg",
    name="yolo11s_960_900train_100val",
)

Ultralytics uses imgsz as the target training image size, and larger image sizes can improve accuracy but cost more compute/VRAM. It also supports batch=-1 to auto-pick a batch size around GPU capacity, and amp=True for mixed precision to reduce memory use and speed up training. (Ultralytics Documentation) For segmentation, mask_ratio controls mask downsampling resolution, and overlap_mask controls whether masks are merged or kept separately during training. (Ultralytics Documentation)

Your custom_dataset.yaml should look like this:

yaml
path: /path/to/blood_cell_dataset

train: images/train
val: images/val

# No test set now. You can add this later.
# test: images/test

names:
  0: blood_cell

If you have multiple classes, use this instead:

yaml
names:
  0: rbc
  1: wbc
  2: platelet

Ultralytics segmentation labels need one .txt per image, one row per object, with polygon points normalized from 0 to 1: <class-index> <x1> <y1> <x2> <y2> .... (Ultralytics Documentation)

For your GPUs:

python
# RTX 2060 6GB safer settings if 960 gives CUDA OOM:
imgsz=768
batch=-1
mask_ratio=2
overlap_mask=True

python
# RTX 3060 12GB stronger settings:
imgsz=1024
batch=-1
mask_ratio=2
overlap_mask=False

If your blood cells are very small, the most important setting is imgsz, not batch size. Try this order:

text
1. yolo11s-seg.pt, imgsz=960
2. yolo11s-seg.pt, imgsz=1024
3. yolo11m-seg.pt, imgsz=960   # only if RTX 3060 12GB and you want more accuracy

I would not use imgsz=640 for 1280×720 blood-cell segmentation unless you need speed more than mask quality.