Transformers
TensorBoard
Safetensors
vit_mae
pretraining
masked-auto-encoding
Generated from Trainer
Instructions to use jaypratap/vit-pretraining-2024_03_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaypratap/vit-pretraining-2024_03_10 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("jaypratap/vit-pretraining-2024_03_10") model = AutoModelForPreTraining.from_pretrained("jaypratap/vit-pretraining-2024_03_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jaypratap/vit-pretraining-2024_03_10: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/jaypratap/vit-pretraining-2024_03_10/resolve/main/training_args.bin
- Command line
-
hf download hf://jaypratap/vit-pretraining-2024_03_10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jaypratap/vit-pretraining-2024_03_10/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- b5397aea96003af321d43a3618b25ddc7119342c9ff7b1e133e396398b786ebc
- Size of remote file:
- 5.05 kB
- SHA256:
- 4655a09e06be7be2cd35612f142edd36e83b2db2f0532e0046927f2e4c092127
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.