fasterrcnn_coco_pytorch

PyTorch Faster R-CNN Example

This example shows how to build an object detection model on the Penn-Fudan Database using Determined's PyTorch API. This example is adapted from this PyTorch Mask R-CNN tutorial

Files

• model_def.py: The core code for the model. This includes building and compiling the model.

Configuration Files

• const.yaml: Train the model with constant hyperparameter values.
• adaptive.yaml: Perform a hyperparameter search using Determined's state-of-the-art adaptive hyperparameter tuning algorithm.

Data

The current implementation uses the pedestrian detection and segmentation Penn-Fudan Database.

To Run

If you have not yet installed Determined, installation instructions can be found under docs/install-admin.html or at https://docs.determined.ai/latest/index.html

Run the following command: det -m <master host:port> experiment create -f const.yaml .. The other configurations can be run by specifying the appropriate configuration file in place of const.yaml.

Results

Training the model with the hyperparameter settings in const.yaml should yield an IOU of ~0.42.