Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Rifqiakmals/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rifqiakmals/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rifqiakmals/model") 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("Rifqiakmals/model") model = AutoModelForImageClassification.from_pretrained("Rifqiakmals/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Rifqiakmals/model: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/Rifqiakmals/model/resolve/main/training_args.bin
- Command line
-
hf download hf://Rifqiakmals/model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Rifqiakmals/model/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 5d6d3fcc2d0ac9cdd79c1e84a3122c7fb2380a2f11d5c19f3fd719215fc294da
- Size of remote file:
- 4.66 kB
- SHA256:
- 43717c1cec455a979453be54e433b1eebcda2d1b02fc66da8a6d9a57338a0da2
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