Instructions to use timm/resnet152.tv2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet152.tv2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet152.tv2_in1k", pretrained=True) - Transformers
How to use timm/resnet152.tv2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet152.tv2_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.tv2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8141048829b8f7d438dab977e0b00f329201ed09efc5d20b4f625ffa24071b1f
- Size of remote file:
- 242 MB
- SHA256:
- 5008cdcff7af7608155a37baf1b30560be2f6db0c8a98eec247409065d46a9ac
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