| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - pytorch |
| - jax-conversion |
| - transformers |
| - resnet |
| - hil-serl |
| - Lerobot |
| - vision |
| - image-classification |
| library_name: pytorch |
| --- |
| |
| # JAX to PyTorch Converted Model (ResNet-10) |
|
|
| It's done in context of porting `HIL-SERL` paper code (https://hil-serl.github.io/) to `Lerobot` (https://github.com/Lerobot/lerobot). |
| The HF doesn't have ResNet-10 model, which could be pretty usefult for robotics tasks because of it's small size. |
| This model is converted from JAX to PyTorch, and the weights are preserved. |
| ## Model Description |
|
|
| [Brief description of the original model and its purpose] |
|
|
| This model is a PyTorch port of the original JAX implementation. The conversion maintains |
| the original model's architecture and weights while making it accessible to PyTorch users. |
| The original model is from https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/utils/train_utils.py#L103. |
| |
| ## Model Details |
| |
| - **Original Framework:** JAX |
| - **Target Framework:** PyTorch |
| - **Model Architecture:** ResNet-10 (4-stage ResNet with basic blocks) |
| - **Original Model:** HIL-SERL ResNet-10 |
| - **Total Parameters:** 4,905,792 (~4.9M parameters) |
| - **Hidden Sizes:** [64, 128, 256, 512] |
| - **Input:** 3-channel RGB images (128x128) |
| - **Embedding Size:** 64 |
| |
| ## Conversion Process |
| |
| This model was converted using an automated JAX to PyTorch conversion pipeline, ensuring: |
| - Weight preservation |
| - Architecture matching |
| - Numerical stability |
| |
| |
| ## Code |
| |
| https://github.com/helper2424/resnet10 |
| |
| |
| ## Usage |
| ```python |
| from transformers import AutoModel, AutoTokenizer |
| model = AutoModel.from_pretrained("lilkm/resnet10") |
| ``` |
| |
| ## Citation |
| ```bibtex |
|
|
| @misc{resnet10, |
| title = "Resnet10", |
| author = "Eugene Mironov and Khalil Meftah and Adil Zouitine and Michel Aractingi and Ke Wang", |
| month = jan, |
| year = "2025", |
| address = "Online", |
| publisher = "Hugging Face", |
| url = "https://huggingface.co/helper2424/resnet10", |
| } |
|
|
| ``` |
| |
