Instructions to use JustFadjrin/batik-vit-model-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JustFadjrin/batik-vit-model-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JustFadjrin/batik-vit-model-classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("JustFadjrin/batik-vit-model-classification") model = AutoModelForImageClassification.from_pretrained("JustFadjrin/batik-vit-model-classification") - Notebooks
- Google Colab
- Kaggle
| precision recall f1-score support | |
| Aceh_Pintu_Aceh 1.0000 0.7500 0.8571 8 | |
| Bali_Barong 0.8889 1.0000 0.9412 8 | |
| Bali_Merak 0.8889 1.0000 0.9412 8 | |
| DKI_Ondel_Ondel 1.0000 1.0000 1.0000 8 | |
| JawaBarat_Megamendung 1.0000 1.0000 1.0000 8 | |
| JawaTimur_Pring 1.0000 1.0000 1.0000 8 | |
| Kalimantan_Dayak 1.0000 1.0000 1.0000 8 | |
| Lampung_Gajah 0.8750 0.8750 0.8750 8 | |
| Madura_Mataketeran 1.0000 1.0000 1.0000 8 | |
| Maluku_Pala 1.0000 1.0000 1.0000 8 | |
| NTB_Lumbung 0.8000 1.0000 0.8889 8 | |
| Papua_Asmat 1.0000 1.0000 1.0000 8 | |
| Papua_Cendrawasih 1.0000 0.8750 0.9333 8 | |
| Papua_Tifa 1.0000 0.8750 0.9333 8 | |
| Solo_Parang 0.5000 0.3750 0.4286 8 | |
| SulawesiSelatan_Lontara 1.0000 1.0000 1.0000 8 | |
| SumateraBarat_Rumah_Minang 0.8750 0.8750 0.8750 8 | |
| SumateraUtara_Boraspati 1.0000 1.0000 1.0000 8 | |
| Yogyakarta_Kawung 1.0000 1.0000 1.0000 8 | |
| Yogyakarta_Parang 0.5000 0.6250 0.5556 8 | |
| accuracy 0.9125 160 | |
| macro avg 0.9164 0.9125 0.9115 160 | |
| weighted avg 0.9164 0.9125 0.9115 160 | |