Instructions to use Andron00e/CLIPForImageClassification-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/CLIPForImageClassification-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/CLIPForImageClassification-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("Andron00e/CLIPForImageClassification-v1") model = AutoModelForImageClassification.from_pretrained("Andron00e/CLIPForImageClassification-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Andron00e/CLIPForImageClassification-v1: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/Andron00e/CLIPForImageClassification-v1/resolve/main/eval_results.json
- Command line
-
hf download hf://Andron00e/CLIPForImageClassification-v1/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Andron00e/CLIPForImageClassification-v1/resolve/main/eval_results.json
190 Bytes
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.8255, | |
| "eval_loss": 0.8115136623382568, | |
| "eval_runtime": 61.4944, | |
| "eval_samples_per_second": 97.57, | |
| "eval_steps_per_second": 12.196 | |
| } |