Instructions to use HaseLab/mahjong-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use HaseLab/mahjong-models with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("HaseLab/mahjong-models", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Update ResNet-50 classifier after real-image fine-tuning
Browse files- classifier-resnet50.pt +2 -2
classifier-resnet50.pt
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9137340ef518e33bfbe9893b0bfb69871465bed54103693d1f8a8408f7a3da19
|
| 3 |
+
size 94655021
|