---
license: mit
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- biology
- nft
- neurofibrillary tangle
---

# Model Card for Model ID
A Yolo 11 Object detector for [Neurofibrillary tangles](https://en.wikipedia.org/wiki/Neurofibrillary_tangle). 

Model is trained from Dataset provided by Vizcarra et al.
 **Citation:**

 Vizcarra JC, Pearce TM, Dugger BN, Keiser MJ, Gearing M, Crary JF, Kiely EJ, Morris M, White B, Glass JD, Farrell K, Gutman DA. Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles. *Acta Neuropathol Commun*. 2023 Dec 18;11(1):202.

 **DOI:** [10.1186/s40478-023-01691-x](https://doi.org/10.1186/s40478-023-01691-x)
 **PubMed ID (PMID):** [38110981](https://pubmed.ncbi.nlm.nih.gov/38110981/)
 **PubMed Central ID (PMCID):** [PMC10726581](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726581/)


## Model Details
* This Model was trained on immunohistochemically stained slides of brain matter at a resolution of **0.25 microns per pixels**. 
* Slides are tiled into 1280x1280 segments before being fed to the detector.
* Out of band testing appears to produce good results at **.5049 microns per pixel**


## Example Ouput

![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F6668c6eac79894103f8026d5%2FC4N4Q8IReeAclJg60UgeA.png)


### Usage

#### Generating Tiles 
  This model expects 1280x1280 sized images. If you are working with a whole slide like an .svs file you can generate tiles using [openslide](https://openslide.org/api/python/):

```py
tile_width=1280
tile_height=1280
svsfile = "myslide.svs"

slide = openslide.OpenSlide(svsfile)
slide_width, slide_height = slide.level_dimensions[level]
for y in range(0, slide_height, tile_height):
  for x in range(0, slide_width, tile_width):
      region = slide.read_region((x, y), level, (tile_width, tile_height)).convert("RGB")
      filename = f"segments/segment_{x}_{y}.png"
      region.save(filename)
```

#### With The Yolo Command Line
```sh
# pip install ultralytics
yolo predict model=.\yolo11n-nft-detector.pt source=some_tile.png
```

#### With The Yolo API
```py
from ultralytics import YOLO

model_path = "yolo11n-nft-detector.pt"
image_path = "some_tile.png"
model = YOLO(model)
res = model(image_path)
```

## Training Results
🎯 Final Model Performance Summary
Overall Metrics:

* mAP50: 80.8% 
* mAP50-95: 62.3%
* Precision: 72.8% -
* Recall: 74.4% -
* Class Performance:

* Pretangle (Type 0): 71.8% mAP50, 66.3% recall
* Tangle (Type 1): 89.7% mAP50, 82.4% recall
* Speed: 1.2ms inference - 4080 Super