BEVFormer-Tiny (Trained on SimData)
Model Description
This repository contains model weights for BEVFormer-tiny, a camera-only 3D object detection model. These weights have been trained or fine-tuned on the synthetic SimData dataset (a NuScenes-format synthetic dataset containing Highway, Urban, and Parking scenarios).
- Architecture: BEVFormer (Tiny variant)
- Input Modality: Multi-view Camera (6 cameras)
- Training Data: SimData-Dataset
- Framework: PyTorch / MMDetection3D
Model Zoo / Checkpoints
This repository provides three different variations of the trained model. Please select the weight file that best suits your testing needs.
| File Name | Training Strategy | Epochs | Description |
|---|---|---|---|
epoch_24.pth |
Train from Scratch | 24 | Trained directly on the SimData dataset from scratch for 24 epochs. |
epoch_30.pth |
Train from Scratch | 30 | Trained directly on the SimData dataset from scratch for 30 epochs (longer training schedule). |
use_predcheckout_train_on_simdata.pth |
Fine-tuning (Transfer Learning) | 24 | Recommended. Initialized from a model pre-trained on real-world NuScenes data (bevformer_tiny_epoch_24.pth), then fine-tuned on the SimData dataset for an additional 24 epochs. This model usually exhibits better convergence and domain adaptation capabilities. |
(Note: If your file for the 30-epoch run is named differently, please check the file list in the 'Files' tab, but the logic remains as described above.)
How to Use
Since BEVFormer is a custom architecture based on mmdetection3d, you cannot load it directly using AutoModel. You need to use the original BEVFormer codebase and load these weights manually.
1. Download Weights
You can download the specific weight file using the huggingface_hub python library.
from huggingface_hub import hf_hub_download
# Define the repository and the specific file you want
repo_id = "Zgogo0/Bevformer_Simdata" # Replace with your actual Repo ID
# Example: Downloading the fine-tuned model
filename = "use_predcheckout_train_on_simdata.pth"
checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
print(f"Model downloaded to: {checkpoint_path}")