---
license: apache-2.0
tags:
- autonomous-driving
- perception
- bevformer
- 3d-object-detection
- pytorch
- synthetic-data
datasets:
- Zgogo0/simData-Dataset
library_name: pytorch
pipeline_tag: object-detection
---

# 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](https://huggingface.co/datasets/Zgogo0/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.

```python
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}")