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 all_results.json from jaypratap/vit-pretraining-2024_03_10: direct link, hf CLI and curl.
- Browser
- Download file 310 Bytes
-
https://huggingface.co/jaypratap/vit-pretraining-2024_03_10/resolve/main/all_results.json
- Command line
-
hf download hf://jaypratap/vit-pretraining-2024_03_10/all_results.json
-
curl -L -o all_results.json https://huggingface.co/jaypratap/vit-pretraining-2024_03_10/resolve/main/all_results.json
310 Bytes
| { | |
| "epoch": 200.0, | |
| "eval_loss": 0.4444296360015869, | |
| "eval_runtime": 43.289, | |
| "eval_samples_per_second": 79.674, | |
| "eval_steps_per_second": 9.979, | |
| "train_loss": 0.513909718911276, | |
| "train_runtime": 133465.2589, | |
| "train_samples_per_second": 29.286, | |
| "train_steps_per_second": 3.661 | |
| } |