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: [Specify architecture]
- Original Model: [Link to original model]
- Parameters: [Number of parameters]
Conversion Process
This model was converted using an automated JAX to PyTorch conversion pipeline, ensuring:
- Weight preservation
- Architecture matching
- Numerical stability
Usage
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("lilkm/resnet10_test")