File size: 2,262 Bytes
22aa0b2 9bec58f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- image-classification
- pytorch
- awesome-yolov8-models
library_name: ultralytics
library_version: 8.0.21
inference: false
datasets:
- keremberke/painting-style-classification
model-index:
- name: keremberke/yolov8n-painting-classification
results:
- task:
type: image-classification
dataset:
type: keremberke/painting-style-classification
name: painting-style-classification
split: validation
metrics:
- type: accuracy
value: 0.04928 # min: 0.0 - max: 1.0
name: top1 accuracy
- type: accuracy
value: 0.23688 # min: 0.0 - max: 1.0
name: top5 accuracy
---
<div align="center">
<img width="640" alt="keremberke/yolov8n-painting-classification" src="https://huggingface.co/keremberke/yolov8n-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==0.0.23 ultralytics==8.0.21
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('keremberke/yolov8n-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)** |