add ultralytics model card

README.md

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+ 
+---
+tags:
+- ultralyticsplus
+- yolov8
+- ultralytics
+- yolo
+- vision
+- image-classification
+- pytorch
+- awesome-yolov8-models
+library_name: ultralytics
+library_version: 8.0.23
+inference: false
+
+datasets:
+- keremberke/painting-style-classification
+
+model-index:
+- name: keremberke/yolov8m-painting-classification
+  results:
+  - task:
+      type: image-classification
+
+    dataset:
+      type: keremberke/painting-style-classification
+      name: painting-style-classification
+      split: validation
+
+    metrics:
+      - type: accuracy
+        value: 0.05723  # min: 0.0 - max: 1.0
+        name: top1 accuracy
+      - type: accuracy
+        value: 0.21463  # min: 0.0 - max: 1.0
+        name: top5 accuracy
+---
+
+<div align="center">
+  <img width="640" alt="keremberke/yolov8m-painting-classification" src="https://huggingface.co/keremberke/yolov8m-painting-classification/resolve/main/thumbnail.jpg">
+</div>
+
+### Supported Labels
+
+```
+['Abstract_Expressionism', 'Action_painting', 'Analytical_Cubism', 'Art_Nouveau_Modern', 'Baroque', 'Color_Field_Painting', 'Contemporary_Realism', 'Cubism', 'Early_Renaissance', 'Expressionism', 'Fauvism', 'High_Renaissance', 'Impressionism', 'Mannerism_Late_Renaissance', 'Minimalism', 'Naive_Art_Primitivism', 'New_Realism', 'Northern_Renaissance', 'Pointillism', 'Pop_Art', 'Post_Impressionism', 'Realism', 'Rococo', 'Romanticism', 'Symbolism', 'Synthetic_Cubism', 'Ukiyo_e']
+```
+
+### How to use
+
+- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
+
+```bash
+pip install ultralyticsplus==0.0.24 ultralytics==8.0.23
+```
+
+- Load model and perform prediction:
+
+```python
+from ultralyticsplus import YOLO, postprocess_classify_output
+
+# load model
+model = YOLO('keremberke/yolov8m-painting-classification')
+
+# set model parameters
+model.overrides['conf'] = 0.25  # model confidence threshold
+
+# set image
+image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
+
+# perform inference
+results = model.predict(image)
+
+# observe results
+print(results[0].probs) # [0.1, 0.2, 0.3, 0.4]
+processed_result = postprocess_classify_output(model, result=results[0])
+print(processed_result) # {"cat": 0.4, "dog": 0.6}
+```
+