Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jaypratap/vit-mae-base-effusion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaypratap/vit-mae-base-effusion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jaypratap/vit-mae-base-effusion-classifier") 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("jaypratap/vit-mae-base-effusion-classifier") model = AutoModelForImageClassification.from_pretrained("jaypratap/vit-mae-base-effusion-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jaypratap/vit-mae-base-effusion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/jaypratap/vit-mae-base-effusion-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://jaypratap/vit-mae-base-effusion-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jaypratap/vit-mae-base-effusion-classifier/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 518269a3b9dc05412b15475b2a6b69c7b6b5850d2b0f65f71c9332cc8c0cb97f
- Size of remote file:
- 4.98 kB
- SHA256:
- 220e1ff1582226d1674ea3b0d62ead5ba655cdd293afaa5e5823e504280fe983
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