Image Classification
PyTorch
Safetensors
Transformers
English
resnet10
feature-extraction
jax-conversion
resnet
hil-serl
Lerobot
vision
custom_code
Instructions to use lilkm/resnet10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilkm/resnet10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lilkm/resnet10", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lilkm/resnet10", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from lilkm/resnet10: direct link, hf CLI and curl.
- Browser
- Download file 19.6 MB
-
https://huggingface.co/lilkm/resnet10/resolve/main/model.safetensors
- Command line
-
hf download hf://lilkm/resnet10/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/lilkm/resnet10/resolve/main/model.safetensors
19.6 MB
- Xet hash:
- 14d6f82ae8c3d7079d999dda8bccf13571711b41fa1c4d267c9d8a60374b4e2f
- Size of remote file:
- 19.6 MB
- SHA256:
- 1be729cd30827ec597a7601d7b10ce1d6a50729b79119142761fbb9bba090d5f
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