Dobb-E: Home Pretrained Robot Vision Model
| Property | Value |
|---|---|
| Parameter Count | 21.3M parameters |
| Model Type | ResNet34 Vision Model |
| License | MIT |
| Framework | PyTorch (via timm) |
| Paper | On Bringing Robots Home |
What is dobb-e?
Dobb-E is an innovative Home Pretrained Representation (HPR) model designed specifically for robotics applications in home environments. Built on a ResNet34 architecture, it's trained on the Homes of New York (HoNY) dataset to enable robots to better understand and navigate domestic settings.
Implementation Details
The model is implemented using the timm library and can be easily loaded using Hugging Face's model hub. It utilizes F32 tensor types and is optimized for vision-based robotics tasks. The implementation is straightforward through timm's create_model function.
- Built on ResNet34 architecture
- Trained on custom HoNY dataset
- Integrates with timm library
- Uses F32 precision
Core Capabilities
- Home environment understanding and navigation
- Visual representation learning for robotics
- Domestic scene comprehension
- Integration with robotic systems
Frequently Asked Questions
Q: What makes this model unique?
Dobb-E is specifically designed and trained for home robotics applications, using a custom dataset of home environments (HoNY). This specialization makes it particularly effective for robots operating in domestic settings.
Q: What are the recommended use cases?
The model is ideal for robotic applications requiring visual understanding of home environments, including navigation, object recognition, and scene understanding in domestic settings.
