Update README.md

README.md

@@ -1,10 +1,56 @@
 ---
 license: apache-2.0
+tags:
+- autonomous-driving
+- perception
+- bevformer
+- 3d-object-detection
+- pytorch
+- synthetic-data
 datasets:
 - Zgogo0/simData-Dataset
-language:
-- en
-base_model:
-- ANIYA673/bevformer
+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}")