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 eval_results.json from Dugerij/image_segmentation_classifier: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
-
https://huggingface.co/Dugerij/image_segmentation_classifier/resolve/main/eval_results.json
- Command line
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hf download hf://Dugerij/image_segmentation_classifier/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Dugerij/image_segmentation_classifier/resolve/main/eval_results.json
205 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9993024066968957, | |
| "eval_loss": 0.003324420191347599, | |
| "eval_runtime": 127.0066, | |
| "eval_samples_per_second": 22.574, | |
| "eval_steps_per_second": 2.827 | |
| } |