Image Classification
Transformers
Safetensors
English
model_hub_mixin
pytorch_model_hub_mixin
Eval Results (legacy)
Instructions to use X01D/6DRepNET-RepVGGA0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use X01D/6DRepNET-RepVGGA0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="X01D/6DRepNET-RepVGGA0") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("X01D/6DRepNET-RepVGGA0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Push model using huggingface_hub.
Browse files- README.md +9 -0
- config.json +6 -0
- model.safetensors +3 -0
README.md
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---
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tags:
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- model_hub_mixin
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- pytorch_model_hub_mixin
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---
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This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
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- Library: [More Information Needed]
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- Docs: [More Information Needed]
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config.json
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{
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"backbone_file": "",
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"backbone_name": "RepVGG-A0",
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"deploy": true,
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"pretrained": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:39b4dd74fdbdd9941fa4bbe5fc003a02d1b53b7f3086f44c87f7f1cb3ba06cf3
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size 28148648
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