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
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("helper2424/resnet10")
Citation
@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",
}