
<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}
```

**More models available at: [awesome-yolov8-models](https://yolov8.xyz)**
{"library_name": "ultralytics", "tags": ["ultralyticsplus", "yolov8", "ultralytics", "yolo", "vision", "image-classification", "pytorch", "awesome-yolov8-models"], "datasets": ["keremberke/painting-style-classification"], "library_version": "8.0.23", "inference": false, "model-index": [{"name": "keremberke/yolov8m-painting-classification", "results": [{"task": {"type": "image-classification"}, "dataset": {"name": "painting-style-classification", "type": "keremberke/painting-style-classification", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.05723, "name": "top1 accuracy"}, {"type": "accuracy", "value": 0.21463, "name": "top5 accuracy"}]}]}]}
ultralytics tensorboard v8 ultralyticsplus yolov8 yolo vision image-classification pytorch awesome-yolov8-models dataset:keremberke/painting-style-classification model-index region:us
TAGS
#ultralytics #tensorboard #v8 #ultralyticsplus #yolov8 #yolo #vision #image-classification #pytorch #awesome-yolov8-models #dataset-keremberke/painting-style-classification #model-index #region-us 


<div align="center">
  <img width="640" alt="keremberke/yolov8m-painting-classification" src="URL
</div>

### Supported Labels



### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models
### Supported Labels ### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models
TAGS
#ultralytics #tensorboard #v8 #ultralyticsplus #yolov8 #yolo #vision #image-classification #pytorch #awesome-yolov8-models #dataset-keremberke/painting-style-classification #model-index #region-us 
 ### Supported Labels ### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models
TAGS
#ultralytics #tensorboard #v8 #ultralyticsplus #yolov8 #yolo #vision #image-classification #pytorch #awesome-yolov8-models #dataset-keremberke/painting-style-classification #model-index #region-us 
### Supported Labels### How to use

- Install ultralyticsplus:



- Load model and perform prediction:



More models available at: awesome-yolov8-models