Instructions to use timm/resnet152.a1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet152.a1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet152.a1_in1k", pretrained=True) - Transformers
How to use timm/resnet152.a1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet152.a1_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/resnet152.a1_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
449ee9b | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:840dddf2a08a196d90a304a653ebbe9cd726197b49afb14e62f587695aedc1f6
size 241682973
|