Instructions to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia") model = AutoModelForImageClassification.from_pretrained("yashshinde0080/regnet_y_400mf-mlxim-finetuned-chest_xray-pneumonia", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "total_flos": 4.975400461644104e+17, | |
| "train_loss": 0.3838548327351476, | |
| "train_runtime": 9417.8372, | |
| "train_samples_per_second": 2.99, | |
| "train_steps_per_second": 0.024 | |
| } |