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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lilkm/resnet10", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +2 -1
preprocessor_config.json
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{
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"crop_pct": 1,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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0.456,
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0.406
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],
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"image_processor_type": "
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"image_std": [
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0.229,
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0.224,
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{
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"crop_pct": 1,
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"data_format": "channels_first",
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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0.456,
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0.406
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],
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"image_processor_type": "ConvNextImageProcessorFast",
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"image_std": [
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0.229,
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0.224,
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