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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - model_hub_mixin | |
| - pytorch_model_hub_mixin | |
| license: apache-2.0 | |
| language: | |
| - en | |
| metrics: | |
| - mae | |
| datasets: | |
| - ETHZurich/biwi_kinect_head_pose | |
| pipeline_tag: image-classification | |
| model-index: | |
| - name: 6DRepNet-RepVGGA0 | |
| results: | |
| - task: | |
| type: Image-Classification | |
| dataset: | |
| name: BIWI | |
| type: Benchmarkingdataset | |
| metrics: | |
| - name: MAE | |
| type: MAE | |
| value: 3.70 | |
| verified: false | |
| This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: | |
| - Library: | |
| - | |
| - Docs: | |
| A reduced version of 6DRepNet model using the backbone of RepVGG A0 backbone | |
| --- | |