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--- |
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license: apache-2.0 |
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tags: |
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- autonomous-driving |
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- perception |
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- bevformer |
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- 3d-object-detection |
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- pytorch |
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- synthetic-data |
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datasets: |
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- Zgogo0/simData-Dataset |
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library_name: pytorch |
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pipeline_tag: object-detection |
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--- |
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# BEVFormer-Tiny (Trained on SimData) |
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## Model Description |
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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). |
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- **Architecture**: BEVFormer (Tiny variant) |
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- **Input Modality**: Multi-view Camera (6 cameras) |
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- **Training Data**: [SimData-Dataset](https://huggingface.co/datasets/Zgogo0/simData-Dataset) |
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- **Framework**: PyTorch / MMDetection3D |
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## Model Zoo / Checkpoints |
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This repository provides three different variations of the trained model. Please select the weight file that best suits your testing needs. |
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| File Name | Training Strategy | Epochs | Description | |
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| :--- | :--- | :--- | :--- | |
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| **`epoch_24.pth`** | **Train from Scratch** | 24 | Trained directly on the SimData dataset from scratch for 24 epochs. | |
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| **`epoch_30.pth`** | **Train from Scratch** | 30 | Trained directly on the SimData dataset from scratch for 30 epochs (longer training schedule). | |
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| **`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. | |
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*(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.)* |
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## How to Use |
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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. |
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### 1. Download Weights |
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You can download the specific weight file using the `huggingface_hub` python library. |
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```python |
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from huggingface_hub import hf_hub_download |
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# Define the repository and the specific file you want |
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repo_id = "Zgogo0/Bevformer_Simdata" # Replace with your actual Repo ID |
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# Example: Downloading the fine-tuned model |
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filename = "use_predcheckout_train_on_simdata.pth" |
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checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename) |
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print(f"Model downloaded to: {checkpoint_path}") |
