All the parameters we used can be found in train_waymo.yaml and train_tdt4265.yaml
Set up the Python/Anaconda environment as done in assignment 4, and install the requirements:
pip install -r requirements.txt
Run setup_waymo.py and update_tdt4265_dataset.py.
Train on the Waymo dataset:
python train.py configs/train_waymo.yaml
Transfer the model:
python transfer_learn.py configs/train_waymo.yaml
Train on the TDT4265 dataset:
python train.py configs/train_tdt4265.yaml
Finally run the evaluation script.
For the backbone we use a ResNet-34 (pretrained on ImageNet), with some modifications, for the first three output feature banks. For the last three feature banks we used additional layers of ResNet BasicBlocks, each of size 2 and with the first block downsampling.
| Layer | Output |
|---|---|
| Conv2d(3, 64) kernel 7x7, stride 2x2, padding 3x3 | |
| BatchNorm2d(64) | |
| ReLU | |
| ResNet-34 layer 1-2 (extra downsampling BasicBlock at the end) | Feature bank 1 (128 x 38 x 38) |
| ResNet-34 layer 3 | Feature bank 2 (256 x 19 x 19) |
| ResNet-34 layer 4 | Feature bank 3 (512 x 10 x 10) |
| Additional layer of two BasicBlocks (first downsampling) | Feature bank 4 (256 x 5 x 5) |
| Additional layer of two BasicBlocks (first downsampling) | Feature bank 5 (256 x 3 x 3) |
| Additional layer of two BasicBlocks (first downsampling) | |
| Conv2d(128, 128) kernel 3x3, stride 2x2, padding 1x1 | |
| BatchNorm2d(128) | |
| ReLU | Feature bank 6 (128 x 1 x 1) |