Instructions to use Dugerij/image_segmentation_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dugerij/image_segmentation_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dugerij/image_segmentation_classifier") 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("Dugerij/image_segmentation_classifier") model = AutoModelForImageClassification.from_pretrained("Dugerij/image_segmentation_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Dugerij/image_segmentation_classifier: direct link, hf CLI and curl.
- Browser
- Download file 209 Bytes
-
https://huggingface.co/Dugerij/image_segmentation_classifier/resolve/main/train_results.json
- Command line
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hf download hf://Dugerij/image_segmentation_classifier/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Dugerij/image_segmentation_classifier/resolve/main/train_results.json
209 Bytes
| { | |
| "epoch": 5.0, | |
| "total_flos": 6.293899396497162e+18, | |
| "train_loss": 0.00641422240726846, | |
| "train_runtime": 5338.9651, | |
| "train_samples_per_second": 15.213, | |
| "train_steps_per_second": 1.902 | |
| } |