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
Update README.md
Browse files
README.md
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@@ -16,6 +16,8 @@ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://h
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torchvision==0.17.1
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- Docs:
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A reduced version of 6DRepNet model using the backbone of RepVGG A0 backbone
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model-index:
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- name: 6DRepNet-RepVGGA0
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results:
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type: head pose estimation
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dataset:
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name: BIWI
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type: Benchmarking
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metrics:
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- name: MAE
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type: MAE
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torchvision==0.17.1
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- Docs:
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A reduced version of 6DRepNet model using the backbone of RepVGG A0 backbone
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---
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model-index:
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- name: 6DRepNet-RepVGGA0
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results:
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type: head pose estimation
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dataset:
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name: BIWI
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type: Benchmarking dataset
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metrics:
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- name: MAE
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type: MAE
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