Update README.md

README.md

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----
-license: mit
----
+---
+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](https://cdn-uploads.huggingface.co/production/uploads/6668c6eac79894103f8026d5/C4N4Q8IReeAclJg60UgeA.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