Instructions to use fxmarty/resnet-tiny-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fxmarty/resnet-tiny-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fxmarty/resnet-tiny-beans") 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("fxmarty/resnet-tiny-beans") model = AutoModelForImageClassification.from_pretrained("fxmarty/resnet-tiny-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from fxmarty/resnet-tiny-beans: direct link, hf CLI and curl.
- Browser
- Download file 203 Bytes
-
https://huggingface.co/fxmarty/resnet-tiny-beans/resolve/main/eval_results.json
- Command line
-
hf download hf://fxmarty/resnet-tiny-beans/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/fxmarty/resnet-tiny-beans/resolve/main/eval_results.json
203 Bytes
| { | |
| "epoch": 6.0, | |
| "eval_accuracy": 0.7368421052631579, | |
| "eval_loss": 0.7412705421447754, | |
| "eval_runtime": 0.7249, | |
| "eval_samples_per_second": 183.475, | |
| "eval_steps_per_second": 23.452 | |
| } |