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", device_map="auto") 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
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: 3.7
verified: false
This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library:
- Docs: A reduced version of 6DRepNet model using the backbone of RepVGG A0 backbone