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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_info.json from JustFadjrin/batik-vit-model-classification: direct link, hf CLI and curl.
- Browser
- Download file 960 Bytes
-
https://huggingface.co/JustFadjrin/batik-vit-model-classification/resolve/main/model_info.json
- Command line
-
hf download hf://JustFadjrin/batik-vit-model-classification/model_info.json
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curl -L -o model_info.json https://huggingface.co/JustFadjrin/batik-vit-model-classification/resolve/main/model_info.json
960 Bytes
| { | |
| "model_name": "google/vit-base-patch16-224-in21k", | |
| "num_classes": 20, | |
| "class_names": [ | |
| "Aceh_Pintu_Aceh", | |
| "Bali_Barong", | |
| "Bali_Merak", | |
| "DKI_Ondel_Ondel", | |
| "JawaBarat_Megamendung", | |
| "JawaTimur_Pring", | |
| "Kalimantan_Dayak", | |
| "Lampung_Gajah", | |
| "Madura_Mataketeran", | |
| "Maluku_Pala", | |
| "NTB_Lumbung", | |
| "Papua_Asmat", | |
| "Papua_Cendrawasih", | |
| "Papua_Tifa", | |
| "Solo_Parang", | |
| "SulawesiSelatan_Lontara", | |
| "SumateraBarat_Rumah_Minang", | |
| "SumateraUtara_Boraspati", | |
| "Yogyakarta_Kawung", | |
| "Yogyakarta_Parang" | |
| ], | |
| "train_samples": 512, | |
| "validation_samples": 128, | |
| "test_samples": 160, | |
| "test_accuracy": 0.9125, | |
| "test_macro_f1": 0.9114589169000935, | |
| "average_confidence_all": 0.7706013917922974, | |
| "average_confidence_correct": 0.7880493402481079, | |
| "average_margin_all": 0.728288471698761, | |
| "recommended_threshold_high": 0.6, | |
| "recommended_threshold_medium": 0.4, | |
| "recommended_margin": 0.2 | |
| } |