Instructions to use nqvii/resnet50_fold_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/resnet50_fold_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/resnet50_fold_2") 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("nqvii/resnet50_fold_2") model = AutoModelForImageClassification.from_pretrained("nqvii/resnet50_fold_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db1bd142d901ba35a234ebe5fe43ffb6efc027ecb150668079eb5a36996d4a3f
- Size of remote file:
- 5.2 kB
- SHA256:
- 904a7e0ee6ca66db270d1ffacae5767b0fd3e02c320ea7e61e0daeb96332fde7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.