Instructions to use timm/seresnet33ts.ra2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/seresnet33ts.ra2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/seresnet33ts.ra2_in1k", pretrained=True) - Transformers
How to use timm/seresnet33ts.ra2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/seresnet33ts.ra2_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/seresnet33ts.ra2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbb5fb4bd647fd0dddd1592b2055ab42f0dc4e10ed365e87517647455ab32a60
- Size of remote file:
- 79.4 MB
- SHA256:
- 7f552f225e82f08f4a22fe37ac731c10033a073726351c5862d1f1c1179fbbd0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.