Instructions to use Efferbach/mobilevit-small-10k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Efferbach/mobilevit-small-10k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Efferbach/mobilevit-small-10k-steps")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Efferbach/mobilevit-small-10k-steps") model = MobileViTForSemanticSegmentation.from_pretrained("Efferbach/mobilevit-small-10k-steps", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Efferbach/mobilevit-small-10k-steps: direct link, hf CLI and curl.
- Browser
- Download file 170 Bytes
-
https://huggingface.co/Efferbach/mobilevit-small-10k-steps/resolve/main/train_results.json
- Command line
-
hf download hf://Efferbach/mobilevit-small-10k-steps/train_results.json
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curl -L -o train_results.json https://huggingface.co/Efferbach/mobilevit-small-10k-steps/resolve/main/train_results.json
170 Bytes
| { | |
| "epoch": 25.97, | |
| "train_loss": 0.10369874272346497, | |
| "train_runtime": 11613.3095, | |
| "train_samples_per_second": 6.889, | |
| "train_steps_per_second": 0.861 | |
| } |