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
File size: 1,634 Bytes
6927a5b | 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 | 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
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