diff --git a/README.md b/README.md
index 0525c176..c86e3619 100644
--- a/README.md
+++ b/README.md
@@ -1,60 +1,72 @@
-# SECOND for KITTI object detection
-SECOND detector. Based on my unofficial implementation of VoxelNet with some improvements.
+# PointPillars
 
-ONLY support python 3.6+, pytorch 0.4.1+. Don't support pytorch 0.4.0. Tested in Ubuntu 16.04/18.04.
+Welcome to PointPillars.
 
-* Ubuntu 18.04 have speed problem in my environment and may can't build/usr SparseConvNet.
+This repo demonstrates how to reproduce the results from
+_PointPillars: Fast Encoders for Object Detection from Point Clouds_ on the
+[KITTI dataset](http://www.cvlibs.net/datasets/kitti/) by making the minimum required changes from the preexisting
+open source codebase [SECOND](https://github.com/traveller59/second.pytorch). 
 
-### Performance in KITTI validation set (50/50 split, people have problems, need to be tuned.)
+This is not an official nuTonomy codebase, but it can be used to match the published PointPillars results.
 
-```
-Car AP@0.70, 0.70, 0.70:
-bbox AP:90.80, 88.97, 87.52
-bev  AP:89.96, 86.69, 86.11
-3d   AP:87.43, 76.48, 74.66
-aos  AP:90.68, 88.39, 86.57
-Car AP@0.70, 0.50, 0.50:
-bbox AP:90.80, 88.97, 87.52
-bev  AP:90.85, 90.02, 89.36
-3d   AP:90.85, 89.86, 89.05
-aos  AP:90.68, 88.39, 86.57
-```
+![Example Results](https://raw.githubusercontent.com/nutonomy/second.pytorch/master/images/pointpillars_kitti_results.pdf)
+
+
+## Getting Started
+
+This is a fork of [SECOND for KITTI object detection](https://github.com/traveller59/second.pytorch) and the relevant
+subset of the original README is reproduced here.
+
+### Code Support
+
+ONLY supports python 3.6+, pytorch 0.4.1+. Code has only been tested on Ubuntu 16.04/18.04.
 
-## Install
+### Install
 
-### 1. Clone code
+#### 1. Clone code
 
 ```bash
-git clone https://github.com/traveller59/second.pytorch.git
-cd ./second.pytorch/second
+git clone https://github.com/nutonomy/second.pytorch.git
 ```
 
-### 2. Install dependence python packages
+#### 2. Install Python packages
 
-It is recommend to use Anaconda package manager.
+It is recommend to use the Anaconda package manager.
 
+First, use Anaconda to configure as many packages as possible.
 ```bash
-pip install shapely fire pybind11 tensorboardX protobuf scikit-image numba pillow
+conda create -n pointpillars python=3.7 anaconda
+source activate pointpillars
+conda install shapely pybind11 protobuf scikit-image numba pillow
+conda install pytorch torchvision -c pytorch
+conda install google-sparsehash -c bioconda
 ```
 
-If you don't have Anaconda:
-
+Then use pip for the packages missing from Anaconda.
 ```bash
-pip install numba
+pip install --upgrade pip
+pip install fire tensorboardX
 ```
 
-Follow instructions in https://github.com/facebookresearch/SparseConvNet to install SparseConvNet.
+Finally, install SparseConvNet. This is not required for PointPillars, but the general SECOND code base expects this
+to be correctly configured. 
+```bash
+git clone git@github.com:facebookresearch/SparseConvNet.git
+cd SparseConvNet/
+bash build.sh
+# NOTE: if bash build.sh fails, try bash develop.sh instead
+```
 
-Install Boost geometry:
+Additionally, you may need to install Boost geometry:
 
 ```bash
 sudo apt-get install libboost-all-dev
 ```
 
 
-### 3. Setup cuda for numba
+#### 3. Setup cuda for numba
 
-you need to add following environment variable for numba.cuda, you can add them to ~/.bashrc:
+You need to add following environment variables for numba to ~/.bashrc:
 
 ```bash
 export NUMBAPRO_CUDA_DRIVER=/usr/lib/x86_64-linux-gnu/libcuda.so
@@ -62,11 +74,13 @@ export NUMBAPRO_NVVM=/usr/local/cuda/nvvm/lib64/libnvvm.so
 export NUMBAPRO_LIBDEVICE=/usr/local/cuda/nvvm/libdevice
 ```
 
-### 4. add second.pytorch/ to PYTHONPATH
+#### 4. PYTHONPATH
 
-## Prepare dataset
+Add second.pytorch/ to your PYTHONPATH.
 
-* Dataset preparation
+### Prepare dataset
+
+#### 1. Dataset preparation
 
 Download KITTI dataset and create some directories first:
 
@@ -85,27 +99,29 @@ Download KITTI dataset and create some directories first:
            └── velodyne_reduced <-- empty directory
 ```
 
-* Create kitti infos:
+Note: PointPillar's protos use ```KITTI_DATASET_ROOT=/data/sets/kitti_second/```.
+
+#### 2. Create kitti infos:
 
 ```bash
 python create_data.py create_kitti_info_file --data_path=KITTI_DATASET_ROOT
 ```
 
-* Create reduced point cloud:
+#### 3. Create reduced point cloud:
 
 ```bash
 python create_data.py create_reduced_point_cloud --data_path=KITTI_DATASET_ROOT
 ```
 
-* Create groundtruth-database infos:
+#### 4. Create groundtruth-database infos:
 
 ```bash
 python create_data.py create_groundtruth_database --data_path=KITTI_DATASET_ROOT
 ```
 
-* Modify config file
+#### 5. Modify config file
 
-There is some path need to be configured in config file:
+The config file needs to be edited to point to the above datasets:
 
 ```bash
 train_input_reader: {
@@ -125,135 +141,28 @@ eval_input_reader: {
 }
 ```
 
-## Usage
 
-### train
+### Train
 
 ```bash
-python ./pytorch/train.py train --config_path=./configs/car.config --model_dir=/path/to/model_dir
+cd ~/second.pytorch/second
+python ./pytorch/train.py train --config_path=./configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir
 ```
 
-* Make sure "/path/to/model_dir" doesn't exist if you want to train new model. A new directory will be created if the model_dir doesn't exist, otherwise will read checkpoints in it.
+* If you want to train a new model, make sure "/path/to/model_dir" doesn't exist.
+* If "/path/to/model_dir" does exist, training will be resumed from the last checkpoint.
+* Training only supports a single GPU. 
+* Training uses a batchsize=2 which should fit in memory on most standard GPUs.
+* On a single 1080Ti, training xyres_16 requires approximately 20 hours for 160 epochs.
 
-* training process use batchsize=3 as default for 1080Ti, you need to reduce batchsize if your GPU has less memory.
 
-* Currently only support single GPU training, but train a model only needs 20 hours (165 epoch) in a single 1080Ti and only needs 40 epoch to reach 74 AP in car moderate 3D in Kitti validation dateset.
+### Evaluate
 
-### evaluate
 
 ```bash
-python ./pytorch/train.py evaluate --config_path=./configs/car.config --model_dir=/path/to/model_dir
-```
-
-* detection result will saved as a result.pkl file in model_dir/eval_results/step_xxx or save as official KITTI label format if you use --pickle_result=False.
-
-### pretrained model
-
-Before using pretrained model, you need to modify some file in SparseConvNet because the pretrained model doesn't support SparseConvNet master:
-
-* convolution.py
-```Python
-# self.weight = Parameter(torch.Tensor(
-#     self.filter_volume, nIn, nOut).normal_(
-#     0,
-#     std))
-self.weight = Parameter(torch.Tensor(
-    self.filter_volume * nIn, nOut).normal_(
-    0,
-    std))
-# ...
-# output.features = ConvolutionFunction.apply(
-#     input.features,
-#     self.weight,
-output.features = ConvolutionFunction.apply(
-    input.features,
-    self.weight.view(self.filter_volume, self.nIn, self.nOut),
-```
-
-* submanifoldConvolution.py
-```Python
-# self.weight = Parameter(torch.Tensor(
-#     self.filter_volume, nIn, nOut).normal_(
-#     0,
-#     std))
-self.weight = Parameter(torch.Tensor(
-    self.filter_volume * nIn, nOut).normal_(
-    0,
-    std))
-# ...
-# output.features = SubmanifoldConvolutionFunction.apply(
-#     input.features,
-#     self.weight,
-output.features = SubmanifoldConvolutionFunction.apply(
-    input.features,
-    self.weight.view(self.filter_volume, self.nIn, self.nOut),
-```
-
-You can download pretrained models in [google drive](https://drive.google.com/open?id=1eblyuILwbxkJXfIP5QlALW5N_x5xJZhL). The car model is corresponding to car.config, the car_tiny model is corresponding to car.tiny.config and the people model is corresponding to people.config.
-
-## Docker
-
-You can use a prebuilt docker for testing:
+cd ~/second.pytorch/second/
+python pytorch/train.py evaluate --config_path= configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir
 ```
-docker pull scrin/second-pytorch 
-```
-Then run:
-```
-nvidia-docker run -it --rm -v /media/yy/960evo/datasets/:/root/data -v $HOME/pretrained_models:/root/model --ipc=host second-pytorch:latest
-python ./pytorch/train.py evaluate --config_path=./configs/car.config --model_dir=/root/model/car
-...
-```
-
-Currently there is a problem that training and evaluating in docker is very slow.
-
-## Try Kitti Viewer Web
-
-### Major step
-
-1. run ```python ./kittiviewer/backend.py main --port=xxxx``` in your server/local.
-
-2. run ```cd ./kittiviewer/frontend && python -m http.server``` to launch a local web server.
-
-3. open your browser and enter your frontend url (e.g. http://127.0.0.1:8000, default]).
-
-4. input backend url (e.g. http://127.0.0.1:16666)
-
-5. input root path, info path and det path (optional)
-
-6. click load, loadDet (optional), input image index in center bottom of screen and press Enter.
-
-### Inference step
-
-Firstly the load button must be clicked and load successfully.
-
-1. input checkpointPath and configPath.
-
-2. click buildNet.
-
-3. click inference.
-
-![GuidePic](https://raw.githubusercontent.com/traveller59/second.pytorch/master/images/viewerweb.png)
-
-
-
-## Try Kitti Viewer (Deprecated)
-
-You should use kitti viewer based on pyqt and pyqtgraph to check data before training.
-
-run ```python ./kittiviewer/viewer.py```, check following picture to use kitti viewer:
-![GuidePic](https://raw.githubusercontent.com/traveller59/second.pytorch/master/images/simpleguide.png)
-
-## Concepts
-
-
-* Kitti lidar box
-
-A kitti lidar box is consist of 7 elements: [x, y, z, w, l, h, rz], see figure.
-
-![Kitti Box Image](https://raw.githubusercontent.com/traveller59/second.pytorch/master/images/kittibox.png)
-
-All training and inference code use kitti box format. So we need to convert other format to KITTI format before training.
-
-* Kitti camera box
 
-A kitti camera box is consist of 7 elements: [x, y, z, l, h, w, ry].
+* Detection result will saved in model_dir/eval_results/step_xxx.
+* By default, results are stored as a result.pkl file. To save as official KITTI label format use --pickle_result=False.
diff --git a/images/pointpillars_kitti_results.pdf b/images/pointpillars_kitti_results.pdf
new file mode 100644
index 00000000..e49f0a5a
Binary files /dev/null and b/images/pointpillars_kitti_results.pdf differ
diff --git a/second/builder/dataset_builder.py b/second/builder/dataset_builder.py
index 231e1f2c..bc6d2d4c 100644
--- a/second/builder/dataset_builder.py
+++ b/second/builder/dataset_builder.py
@@ -85,6 +85,7 @@ def build(input_reader_config,
         gt_loc_noise_std=list(cfg.groundtruth_localization_noise_std),
         global_rotation_noise=list(cfg.global_rotation_uniform_noise),
         global_scaling_noise=list(cfg.global_scaling_uniform_noise),
+        global_loc_noise_std=(0.2, 0.2, 0.2),
         global_random_rot_range=list(
             cfg.global_random_rotation_range_per_object),
         db_sampler=db_sampler,
diff --git a/second/configs/pointpillars/README.md b/second/configs/pointpillars/README.md
new file mode 100644
index 00000000..6c9cb8f7
--- /dev/null
+++ b/second/configs/pointpillars/README.md
@@ -0,0 +1,3 @@
+# PointPillars Configs
+
+The configuration files in these directories can be used to reproduce the results published in PointPillars.
diff --git a/second/configs/pointpillars/car/xyres_16.proto b/second/configs/pointpillars/car/xyres_16.proto
new file mode 100644
index 00000000..d2b7bdc1
--- /dev/null
+++ b/second/configs/pointpillars/car/xyres_16.proto
@@ -0,0 +1,187 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -39.68, -3, 69.12, 39.68, 1]
+      voxel_size : [0.16, 0.16, 4]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 1
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -39.68, -5, 69.12, 39.68, 5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+         anchor_generator_stride: {
+           sizes: [1.6, 3.9, 1.56] # wlh
+           strides: [0.32, 0.32, 0.0] # if generate only 1 z_center, z_stride will be ignored
+           offsets: [0.16, -39.52, -1.78] # origin_offset + strides / 2
+           rotations: [0, 1.57] # 0, pi/2
+           matched_threshold : 0.6
+           unmatched_threshold : 0.45
+         }
+       }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Car"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Car"
+        value: 15
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Car"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Car"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/car/xyres_20.proto b/second/configs/pointpillars/car/xyres_20.proto
new file mode 100644
index 00000000..0b51cd3c
--- /dev/null
+++ b/second/configs/pointpillars/car/xyres_20.proto
@@ -0,0 +1,187 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -40, -3, 70.4, 40, 1]
+      voxel_size : [0.2, 0.2, 4]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 1
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -40, -5, 70.4, 40, 5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+         anchor_generator_stride: {
+           sizes: [1.6, 3.9, 1.56] # wlh
+           strides: [0.4, 0.4, 0.0] # if generate only 1 z_center, z_stride will be ignored
+           offsets: [0.2, -39.8, -1.78] # origin_offset + strides / 2
+           rotations: [0, 1.57] # 0, pi/2
+           matched_threshold : 0.6
+           unmatched_threshold : 0.45
+         }
+       }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Car"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Car"
+        value: 15
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Car"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Car"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/car/xyres_24.proto b/second/configs/pointpillars/car/xyres_24.proto
new file mode 100644
index 00000000..6a402e72
--- /dev/null
+++ b/second/configs/pointpillars/car/xyres_24.proto
@@ -0,0 +1,187 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -40.32, -3, 71.04, 40.32, 1]
+      voxel_size : [0.24, 0.24, 4]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 1
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -40.32, -5, 71.04, 40.32, 5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+         anchor_generator_stride: {
+           sizes: [1.6, 3.9, 1.56] # wlh
+           strides: [0.48, 0.48, 0.0] # if generate only 1 z_center, z_stride will be ignored
+           offsets: [0.24, -40.08, -1.78] # origin_offset + strides / 2
+           rotations: [0, 1.57] # 0, pi/2
+           matched_threshold : 0.6
+           unmatched_threshold : 0.45
+         }
+       }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Car"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Car"
+        value: 15
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Car"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Car"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/car/xyres_28.proto b/second/configs/pointpillars/car/xyres_28.proto
new file mode 100644
index 00000000..fc14bee2
--- /dev/null
+++ b/second/configs/pointpillars/car/xyres_28.proto
@@ -0,0 +1,187 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -40.32, -3, 71.68, 40.32, 1]
+      voxel_size : [0.28, 0.28, 4]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 1
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [2, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -40.32, -5, 71.68, 40.32, 5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+         anchor_generator_stride: {
+           sizes: [1.6, 3.9, 1.56] # wlh
+           strides: [0.56, 0.56, 0.0] # if generate only 1 z_center, z_stride will be ignored
+           offsets: [0.28, -40.04, -1.78] # origin_offset + strides / 2
+           rotations: [0, 1.57] # 0, pi/2
+           matched_threshold : 0.6
+           unmatched_threshold : 0.45
+         }
+       }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Car"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Car"
+        value: 15
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Car"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Car"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/ped_cycle/xyres_16.proto b/second/configs/pointpillars/ped_cycle/xyres_16.proto
new file mode 100644
index 00000000..eb2abcf7
--- /dev/null
+++ b/second/configs/pointpillars/ped_cycle/xyres_16.proto
@@ -0,0 +1,197 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -19.84, -2.5, 47.36, 19.84, 0.5]
+      voxel_size : [0.16, 0.16, 3]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 2
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [1, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -19.84, -2.5, 47.36, 19.84, 0.5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 1.76, 1.73] # wlh
+          strides: [0.16, 0.16, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.08, -19.76, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 0.8, 1.73] # wlh
+          strides: [0.16, 0.16, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.08, -19.76, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Cyclist"
+        value: 8
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Cyclist"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/ped_cycle/xyres_20.proto b/second/configs/pointpillars/ped_cycle/xyres_20.proto
new file mode 100644
index 00000000..816a33d3
--- /dev/null
+++ b/second/configs/pointpillars/ped_cycle/xyres_20.proto
@@ -0,0 +1,197 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -20, -2.5, 48, 20, 0.5]
+      voxel_size : [0.2, 0.2, 3]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 2
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [1, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -20, -2.5, 48, 20, 0.5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 1.76, 1.73] # wlh
+          strides: [0.2, 0.2, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.1, -19.9, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 0.8, 1.73] # wlh
+          strides: [0.2, 0.2, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.1, -19.9, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Cyclist"
+        value: 8
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Cyclist"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/ped_cycle/xyres_24.proto b/second/configs/pointpillars/ped_cycle/xyres_24.proto
new file mode 100644
index 00000000..a89abbf5
--- /dev/null
+++ b/second/configs/pointpillars/ped_cycle/xyres_24.proto
@@ -0,0 +1,197 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -19.68, -2.5, 48, 19.68, 0.5]
+      voxel_size : [0.24, 0.24, 3]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 2
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [1, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -19.68, -2.5, 48, 19.68, 0.5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 1.76, 1.73] # wlh
+          strides: [0.24, 0.24, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.12, -19.56, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 0.8, 1.73] # wlh
+          strides: [0.24, 0.24, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.12, -19.56, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Cyclist"
+        value: 8
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Cyclist"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/configs/pointpillars/ped_cycle/xyres_28.proto b/second/configs/pointpillars/ped_cycle/xyres_28.proto
new file mode 100644
index 00000000..c75a3fbf
--- /dev/null
+++ b/second/configs/pointpillars/ped_cycle/xyres_28.proto
@@ -0,0 +1,197 @@
+model: {
+  second: {
+    voxel_generator {
+      point_cloud_range : [0, -20.16, -2.5, 47.04, 20.16, 0.5]
+      voxel_size : [0.28, 0.28, 3]
+      max_number_of_points_per_voxel : 100
+    }
+    num_class: 2
+    voxel_feature_extractor: {
+      module_class_name: "PillarFeatureNet"
+      num_filters: [64]
+      with_distance: false
+    }
+    middle_feature_extractor: {
+      module_class_name: "PointPillarsScatter"
+    }
+    rpn: {
+      module_class_name: "RPN"
+      layer_nums: [3, 5, 5]
+      layer_strides: [1, 2, 2]
+      num_filters: [64, 128, 256]
+      upsample_strides: [1, 2, 4]
+      num_upsample_filters: [128, 128, 128]
+      use_groupnorm: false
+      num_groups: 32
+    }
+    loss: {
+      classification_loss: {
+        weighted_sigmoid_focal: {
+          alpha: 0.25
+          gamma: 2.0
+          anchorwise_output: true
+        }
+      }
+      localization_loss: {
+        weighted_smooth_l1: {
+          sigma: 3.0
+          code_weight: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 2.0
+    }
+    # Outputs
+    use_sigmoid_score: true
+    encode_background_as_zeros: true
+    encode_rad_error_by_sin: true
+
+    use_direction_classifier: true
+    direction_loss_weight: 0.2
+    use_aux_classifier: false
+    # Loss
+    pos_class_weight: 1.0
+    neg_class_weight: 1.0
+
+    loss_norm_type: NormByNumPositives
+    # Postprocess
+    post_center_limit_range: [0, -20.16, -2.5, 47.04, 20.16, 0.5]
+    use_rotate_nms: false
+    use_multi_class_nms: false
+    nms_pre_max_size: 1000
+    nms_post_max_size: 300
+    nms_score_threshold: 0.05
+    nms_iou_threshold: 0.5
+
+    use_bev: false
+    num_point_features: 4
+    without_reflectivity: false
+    box_coder: {
+      ground_box3d_coder: {
+        linear_dim: false
+        encode_angle_vector: false
+      }
+    }
+    target_assigner: {
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 1.76, 1.73] # wlh
+          strides: [0.28, 0.28, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.14, -20.02, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+      anchor_generators: {
+        anchor_generator_stride: {
+          sizes: [0.6, 0.8, 1.73] # wlh
+          strides: [0.28, 0.28, 0.0] # if generate only 1 z_center, z_stride will be ignored
+          offsets: [0.14, -20.02, -1.465] # origin_offset + strides / 2
+          rotations: [0, 1.57] # 0, pi/2
+          matched_threshold : 0.5
+          unmatched_threshold : 0.35
+        }
+      }
+
+      sample_positive_fraction : -1
+      sample_size : 512
+      region_similarity_calculator: {
+        nearest_iou_similarity: {
+        }
+      }
+    }
+  }
+}
+
+
+train_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_train.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  max_num_epochs : 160
+  batch_size: 2
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: true
+  num_workers: 2
+  groundtruth_localization_noise_std: [0.25, 0.25, 0.25]
+  groundtruth_rotation_uniform_noise: [-0.15707963267, 0.15707963267]
+  global_rotation_uniform_noise: [-0.78539816, 0.78539816]
+  global_scaling_uniform_noise: [0.95, 1.05]
+  global_random_rotation_range_per_object: [0, 0]
+  anchor_area_threshold: 1
+  remove_points_after_sample: false
+  groundtruth_points_drop_percentage: 0.0
+  groundtruth_drop_max_keep_points: 15
+  database_sampler {
+    database_info_path: "/data/sets/kitti_second/kitti_dbinfos_train.pkl"
+    sample_groups {
+      name_to_max_num {
+        key: "Cyclist"
+        value: 8
+      }
+    }
+    database_prep_steps {
+      filter_by_min_num_points {
+        min_num_point_pairs {
+          key: "Cyclist"
+          value: 5
+        }
+      }
+    }
+    database_prep_steps {
+      filter_by_difficulty {
+        removed_difficulties: [-1]
+      }
+    }
+    global_random_rotation_range_per_object: [0, 0]
+    rate: 1.0
+  }
+
+  remove_unknown_examples: false
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_train.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
+
+train_config: {
+  optimizer: {
+    adam_optimizer: {
+      learning_rate: {
+        exponential_decay_learning_rate: {
+          initial_learning_rate: 0.0002
+          decay_steps: 27840 # 1856 steps per epoch * 15 epochs
+          decay_factor: 0.8
+          staircase: true
+        }
+      }
+      weight_decay: 0.0001
+    }
+    use_moving_average: false
+
+  }
+  inter_op_parallelism_threads: 4
+  intra_op_parallelism_threads: 4
+  steps: 296960 # 1856 steps per epoch * 160 epochs
+  steps_per_eval: 9280 # 1856 steps per epoch * 5 epochs
+  save_checkpoints_secs : 1800 # half hour
+  save_summary_steps : 10
+  enable_mixed_precision: false
+  loss_scale_factor : 512.0
+  clear_metrics_every_epoch: false
+}
+
+eval_input_reader: {
+  record_file_path: "/data/sets/kitti_second/kitti_val.tfrecord"
+  class_names: ["Cyclist", "Pedestrian"]
+  batch_size: 2
+  max_num_epochs : 160
+  prefetch_size : 25
+  max_number_of_voxels: 12000
+  shuffle_points: false
+  num_workers: 3
+  anchor_area_threshold: 1
+  remove_environment: false
+  kitti_info_path: "/data/sets/kitti_second/kitti_infos_val.pkl"
+  kitti_root_path: "/data/sets/kitti_second"
+}
diff --git a/second/core/preprocess.py b/second/core/preprocess.py
index 3a80e4c1..98e0f41f 100644
--- a/second/core/preprocess.py
+++ b/second/core/preprocess.py
@@ -885,3 +885,20 @@ def box_collision_test(boxes, qboxes, clockwise=True):
                             ret[i, j] = True  # collision.
     return ret
 
+
+def global_translate(gt_boxes, points, noise_translate_std):
+    """
+    Apply global translation to gt_boxes and points.
+    """
+
+    if not isinstance(noise_translate_std, (list, tuple, np.ndarray)):
+        noise_translate_std = np.array([noise_translate_std, noise_translate_std, noise_translate_std])
+
+    noise_translate = np.array([np.random.normal(0, noise_translate_std[0], 1),
+                                np.random.normal(0, noise_translate_std[1], 1),
+                                np.random.normal(0, noise_translate_std[0], 1)]).T
+
+    points[:, :3] += noise_translate
+    gt_boxes[:, :3] += noise_translate
+
+    return gt_boxes, points
diff --git a/second/data/preprocess.py b/second/data/preprocess.py
index 9e7f4a45..436f55dd 100644
--- a/second/data/preprocess.py
+++ b/second/data/preprocess.py
@@ -59,6 +59,7 @@ def prep_pointcloud(input_dict,
                     gt_loc_noise_std=[1.0, 1.0, 1.0],
                     global_rotation_noise=[-np.pi / 4, np.pi / 4],
                     global_scaling_noise=[0.95, 1.05],
+                    global_loc_noise_std=(0.2, 0.2, 0.2),
                     global_random_rot_range=[0.78, 2.35],
                     generate_bev=False,
                     without_reflectivity=False,
@@ -208,6 +209,9 @@ def prep_pointcloud(input_dict,
         gt_boxes, points = prep.global_scaling_v2(gt_boxes, points,
                                                   *global_scaling_noise)
 
+        # Global translation
+        gt_boxes, points = prep.global_translate(gt_boxes, points, global_loc_noise_std)
+
         bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]]
         mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range)
         gt_boxes = gt_boxes[mask]
diff --git a/second/pytorch/models/pointpillars.py b/second/pytorch/models/pointpillars.py
new file mode 100644
index 00000000..3671080d
--- /dev/null
+++ b/second/pytorch/models/pointpillars.py
@@ -0,0 +1,192 @@
+"""
+PointPillars fork from SECOND.
+Code written by Alex Lang and Oscar Beijbom, 2018.
+Licensed under MIT License [see LICENSE].
+"""
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from second.pytorch.utils import get_paddings_indicator
+from torchplus.nn import Empty
+from torchplus.tools import change_default_args
+
+
+class PFNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 use_norm=True,
+                 last_layer=False):
+        """
+        Pillar Feature Net Layer.
+        The Pillar Feature Net could be composed of a series of these layers, but the PointPillars paper results only
+        used a single PFNLayer. This layer performs a similar role as second.pytorch.voxelnet.VFELayer.
+        :param in_channels: <int>. Number of input channels.
+        :param out_channels: <int>. Number of output channels.
+        :param use_norm: <bool>. Whether to include BatchNorm.
+        :param last_layer: <bool>. If last_layer, there is no concatenation of features.
+        """
+
+        super().__init__()
+        self.name = 'PFNLayer'
+        self.last_vfe = last_layer
+        if not self.last_vfe:
+            out_channels = out_channels // 2
+        self.units = out_channels
+
+        if use_norm:
+            BatchNorm1d = change_default_args(eps=1e-3, momentum=0.01)(nn.BatchNorm1d)
+            Linear = change_default_args(bias=False)(nn.Linear)
+        else:
+            BatchNorm1d = Empty
+            Linear = change_default_args(bias=True)(nn.Linear)
+
+        self.linear = Linear(in_channels, self.units)
+        self.norm = BatchNorm1d(self.units)
+
+    def forward(self, inputs):
+
+        x = self.linear(inputs)
+        x = self.norm(x.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
+        x = F.relu(x)
+
+        x_max = torch.max(x, dim=1, keepdim=True)[0]
+
+        if self.last_vfe:
+            return x_max
+        else:
+            x_repeat = x_max.repeat(1, inputs.shape[1], 1)
+            x_concatenated = torch.cat([x, x_repeat], dim=2)
+            return x_concatenated
+
+
+class PillarFeatureNet(nn.Module):
+    def __init__(self,
+                 num_input_features=4,
+                 use_norm=True,
+                 num_filters=(64,),
+                 with_distance=False,
+                 voxel_size=(0.2, 0.2, 4),
+                 pc_range=(0, -40, -3, 70.4, 40, 1)):
+        """
+        Pillar Feature Net.
+        The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a
+        similar role to SECOND's second.pytorch.voxelnet.VoxelFeatureExtractor.
+        :param num_input_features: <int>. Number of input features, either x, y, z or x, y, z, r.
+        :param use_norm: <bool>. Whether to include BatchNorm.
+        :param num_filters: (<int>: N). Number of features in each of the N PFNLayers.
+        :param with_distance: <bool>. Whether to include Euclidean distance to points.
+        :param voxel_size: (<float>: 3). Size of voxels, only utilize x and y size.
+        :param pc_range: (<float>: 6). Point cloud range, only utilize x and y min.
+        """
+
+        super().__init__()
+        self.name = 'PillarFeatureNet'
+        assert len(num_filters) > 0
+        num_input_features += 5
+        if with_distance:
+            num_input_features += 1
+        self._with_distance = with_distance
+
+        # Create PillarFeatureNet layers
+        num_filters = [num_input_features] + list(num_filters)
+        pfn_layers = []
+        for i in range(len(num_filters) - 1):
+            in_filters = num_filters[i]
+            out_filters = num_filters[i + 1]
+            if i < len(num_filters) - 2:
+                last_layer = False
+            else:
+                last_layer = True
+            pfn_layers.append(PFNLayer(in_filters, out_filters, use_norm, last_layer=last_layer))
+        self.pfn_layers = nn.ModuleList(pfn_layers)
+
+        # Need pillar (voxel) size and x/y offset in order to calculate pillar offset
+        self.vx = voxel_size[0]
+        self.vy = voxel_size[1]
+        self.x_offset = self.vx / 2 + pc_range[0]
+        self.y_offset = self.vy / 2 + pc_range[1]
+
+    def forward(self, features, num_voxels, coors):
+
+        # Find distance of x, y, and z from cluster center
+        points_mean = features[:, :, :3].sum(dim=1, keepdim=True) / num_voxels.type_as(features).view(-1, 1, 1)
+        f_cluster = features[:, :, :3] - points_mean
+
+        # Find distance of x, y, and z from pillar center
+        f_center = features[:, :, :2]
+        f_center[:, :, 0] = f_center[:, :, 0] - (coors[:, 3].float().unsqueeze(1) * self.vx + self.x_offset)
+        f_center[:, :, 1] = f_center[:, :, 1] - (coors[:, 2].float().unsqueeze(1) * self.vy + self.y_offset)
+
+        # Combine together feature decorations
+        features_ls = [features, f_cluster, f_center]
+        if self._with_distance:
+            points_dist = torch.norm(features[:, :, :3], 2, 2, keepdim=True)
+            features_ls.append(points_dist)
+        features = torch.cat(features_ls, dim=-1)
+
+        # The feature decorations were calculated without regard to whether pillar was empty. Need to ensure that
+        # empty pillars remain set to zeros.
+        voxel_count = features.shape[1]
+        mask = get_paddings_indicator(num_voxels, voxel_count, axis=0)
+        mask = torch.unsqueeze(mask, -1).type_as(features)
+        features *= mask
+
+        # Forward pass through PFNLayers
+        for pfn in self.pfn_layers:
+            features = pfn(features)
+
+        return features.squeeze()
+
+
+class PointPillarsScatter(nn.Module):
+    def __init__(self,
+                 output_shape,
+                 num_input_features=64):
+        """
+        Point Pillar's Scatter.
+        Converts learned features from dense tensor to sparse pseudo image. This replaces SECOND's
+        second.pytorch.voxelnet.SparseMiddleExtractor.
+        :param output_shape: ([int]: 4). Required output shape of features.
+        :param num_input_features: <int>. Number of input features.
+        """
+
+        super().__init__()
+        self.name = 'PointPillarsScatter'
+        self.output_shape = output_shape
+        self.ny = output_shape[2]
+        self.nx = output_shape[3]
+        self.nchannels = num_input_features
+
+    def forward(self, voxel_features, coords, batch_size):
+
+        # batch_canvas will be the final output.
+        batch_canvas = []
+        for batch_itt in range(batch_size):
+            # Create the canvas for this sample
+            canvas = torch.zeros(self.nchannels, self.nx * self.ny, dtype=voxel_features.dtype,
+                                 device=voxel_features.device)
+
+            # Only include non-empty pillars
+            batch_mask = coords[:, 0] == batch_itt
+            this_coords = coords[batch_mask, :]
+            indices = this_coords[:, 2] * self.nx + this_coords[:, 3]
+            indices = indices.type(torch.long)
+            voxels = voxel_features[batch_mask, :]
+            voxels = voxels.t()
+
+            # Now scatter the blob back to the canvas.
+            canvas[:, indices] = voxels
+
+            # Append to a list for later stacking.
+            batch_canvas.append(canvas)
+
+        # Stack to 3-dim tensor (batch-size, nchannels, nrows*ncols)
+        batch_canvas = torch.stack(batch_canvas, 0)
+
+        # Undo the column stacking to final 4-dim tensor
+        batch_canvas = batch_canvas.view(batch_size, self.nchannels, self.ny, self.nx)
+
+        return batch_canvas
diff --git a/second/pytorch/models/voxelnet.py b/second/pytorch/models/voxelnet.py
index f85d0db6..f113b34c 100644
--- a/second/pytorch/models/voxelnet.py
+++ b/second/pytorch/models/voxelnet.py
@@ -17,6 +17,8 @@
 from second.pytorch.core.losses import (WeightedSigmoidClassificationLoss,
                                           WeightedSmoothL1LocalizationLoss,
                                           WeightedSoftmaxClassificationLoss)
+from second.pytorch.models.pointpillars import PillarFeatureNet, PointPillarsScatter
+from second.pytorch.utils import get_paddings_indicator
 
 
 def _get_pos_neg_loss(cls_loss, labels):
@@ -36,30 +38,6 @@ def _get_pos_neg_loss(cls_loss, labels):
     return cls_pos_loss, cls_neg_loss
 
 
-def get_paddings_indicator(actual_num, max_num, axis=0):
-    """Create boolean mask by actually number of a padded tensor.
-
-    Args:
-        actual_num ([type]): [description]
-        max_num ([type]): [description]
-
-    Returns:
-        [type]: [description]
-    """
-
-    actual_num = torch.unsqueeze(actual_num, axis + 1)
-    # tiled_actual_num: [N, M, 1]
-    max_num_shape = [1] * len(actual_num.shape)
-    max_num_shape[axis + 1] = -1
-    max_num = torch.arange(
-        max_num, dtype=torch.int, device=actual_num.device).view(max_num_shape)
-    # tiled_actual_num: [[3,3,3,3,3], [4,4,4,4,4], [2,2,2,2,2]]
-    # tiled_max_num: [[0,1,2,3,4], [0,1,2,3,4], [0,1,2,3,4]]
-    paddings_indicator = actual_num.int() > max_num
-    # paddings_indicator shape: [batch_size, max_num]
-    return paddings_indicator
-
-
 class VFELayer(nn.Module):
     def __init__(self, in_channels, out_channels, use_norm=True, name='vfe'):
         super(VFELayer, self).__init__()
@@ -121,7 +99,7 @@ def __init__(self,
         # var_torch_init(self.linear.bias)
         self.norm = BatchNorm1d(num_filters[1])
 
-    def forward(self, features, num_voxels):
+    def forward(self, features, num_voxels, coors):
         # features: [concated_num_points, num_voxel_size, 3(4)]
         # num_voxels: [concated_num_points]
         points_mean = features[:, :, :3].sum(
@@ -183,7 +161,7 @@ def __init__(self,
         # var_torch_init(self.linear.bias)
         self.norm = BatchNorm1d(num_filters[-1])
 
-    def forward(self, features, num_voxels):
+    def forward(self, features, num_voxels, coors):
         # features: [concated_num_points, num_voxel_size, 3(4)]
         # num_voxels: [concated_num_points]
         points_mean = features[:, :, :3].sum(
@@ -587,6 +565,7 @@ def __init__(self,
         vfe_class_dict = {
             "VoxelFeatureExtractor": VoxelFeatureExtractor,
             "VoxelFeatureExtractorV2": VoxelFeatureExtractorV2,
+            "PillarFeatureNet": PillarFeatureNet
         }
         vfe_class = vfe_class_dict[vfe_class_name]
         self.voxel_feature_extractor = vfe_class(
@@ -594,24 +573,32 @@ def __init__(self,
             use_norm,
             num_filters=vfe_num_filters,
             with_distance=with_distance)
-        mid_class_dict = {
-            "MiddleExtractor": MiddleExtractor,
-            "SparseMiddleExtractor": SparseMiddleExtractor,
-        }
-        mid_class = mid_class_dict[middle_class_name]
-        self.middle_feature_extractor = mid_class(
-            output_shape,
-            use_norm,
-            num_input_features=vfe_num_filters[-1],
-            num_filters_down1=middle_num_filters_d1,
-            num_filters_down2=middle_num_filters_d2)
-        if len(middle_num_filters_d2) == 0:
-            if len(middle_num_filters_d1) == 0:
-                num_rpn_input_filters = vfe_num_filters[-1]
-            else:
-                num_rpn_input_filters = middle_num_filters_d1[-1]
+
+        print("middle_class_name", middle_class_name)
+        if middle_class_name == "PointPillarsScatter":
+            self.middle_feature_extractor = PointPillarsScatter(output_shape=output_shape,
+                                                                num_input_features=vfe_num_filters[-1])
+            num_rpn_input_filters = self.middle_feature_extractor.nchannels
         else:
-            num_rpn_input_filters = middle_num_filters_d2[-1]
+            mid_class_dict = {
+                "MiddleExtractor": MiddleExtractor,
+                "SparseMiddleExtractor": SparseMiddleExtractor,
+            }
+            mid_class = mid_class_dict[middle_class_name]
+            self.middle_feature_extractor = mid_class(
+                output_shape,
+                use_norm,
+                num_input_features=vfe_num_filters[-1],
+                num_filters_down1=middle_num_filters_d1,
+                num_filters_down2=middle_num_filters_d2)
+            if len(middle_num_filters_d2) == 0:
+                if len(middle_num_filters_d1) == 0:
+                    num_rpn_input_filters = int(vfe_num_filters[-1] * 2)
+                else:
+                    num_rpn_input_filters = int(middle_num_filters_d1[-1] * 2)
+            else:
+                num_rpn_input_filters = int(middle_num_filters_d2[-1] * 2)
+
         rpn_class_dict = {
             "RPN": RPN,
         }
@@ -624,7 +611,7 @@ def __init__(self,
             num_filters=rpn_num_filters,
             upsample_strides=rpn_upsample_strides,
             num_upsample_filters=rpn_num_upsample_filters,
-            num_input_filters=num_rpn_input_filters * 2,
+            num_input_filters=num_rpn_input_filters,
             num_anchor_per_loc=target_assigner.num_anchors_per_location,
             encode_background_as_zeros=encode_background_as_zeros,
             use_direction_classifier=use_direction_classifier,
@@ -666,7 +653,7 @@ def forward(self, example):
         # features: [num_voxels, max_num_points_per_voxel, 7]
         # num_points: [num_voxels]
         # coors: [num_voxels, 4]
-        voxel_features = self.voxel_feature_extractor(voxels, num_points)
+        voxel_features = self.voxel_feature_extractor(voxels, num_points, coors)
         if self._use_sparse_rpn:
             preds_dict = self.sparse_rpn(voxel_features, coors, batch_size_dev)
         else:
diff --git a/second/pytorch/train.py b/second/pytorch/train.py
index 2d2b143c..4fcf0902 100644
--- a/second/pytorch/train.py
+++ b/second/pytorch/train.py
@@ -98,6 +98,8 @@ def train(config_path,
 
     model_dir = pathlib.Path(model_dir)
     model_dir.mkdir(parents=True, exist_ok=True)
+    eval_checkpoint_dir = model_dir / 'eval_checkpoints'
+    eval_checkpoint_dir.mkdir(parents=True, exist_ok=True)
     if result_path is None:
         result_path = model_dir / 'results'
     config_file_bkp = "pipeline.config"
@@ -335,6 +337,10 @@ def _worker_init_fn(worker_id):
             total_step_elapsed += steps
             torchplus.train.save_models(model_dir, [net, optimizer],
                                         net.get_global_step())
+
+            # Ensure that all evaluation points are saved forever
+            torchplus.train.save_models(eval_checkpoint_dir, [net, optimizer], net.get_global_step(), max_to_keep=100)
+
             net.eval()
             result_path_step = result_path / f"step_{net.get_global_step()}"
             result_path_step.mkdir(parents=True, exist_ok=True)
@@ -379,10 +385,20 @@ def _worker_init_fn(worker_id):
             ]
             if not pickle_result:
                 dt_annos = kitti.get_label_annos(result_path_step)
-            result = get_official_eval_result(gt_annos, dt_annos, class_names)
+            result, mAPbbox, mAPbev, mAP3d, mAPaos = get_official_eval_result(gt_annos, dt_annos, class_names,
+                                                                              return_data=True)
             print(result, file=logf)
             print(result)
             writer.add_text('eval_result', result, global_step)
+
+            for i, class_name in enumerate(class_names):
+                writer.add_scalar('bev_ap:{}'.format(class_name), mAPbev[i, 1, 0], global_step)
+                writer.add_scalar('3d_ap:{}'.format(class_name), mAP3d[i, 1, 0], global_step)
+                writer.add_scalar('aos_ap:{}'.format(class_name), mAPaos[i, 1, 0], global_step)
+            writer.add_scalar('bev_map', np.mean(mAPbev[:, 1, 0]), global_step)
+            writer.add_scalar('3d_map', np.mean(mAP3d[:, 1, 0]), global_step)
+            writer.add_scalar('aos_map', np.mean(mAPaos[:, 1, 0]), global_step)
+
             result = get_coco_eval_result(gt_annos, dt_annos, class_names)
             print(result, file=logf)
             print(result)
diff --git a/second/pytorch/utils.py b/second/pytorch/utils.py
new file mode 100644
index 00000000..e49cbadc
--- /dev/null
+++ b/second/pytorch/utils.py
@@ -0,0 +1,25 @@
+import torch
+
+
+def get_paddings_indicator(actual_num, max_num, axis=0):
+    """Create boolean mask by actually number of a padded tensor.
+
+    Args:
+        actual_num ([type]): [description]
+        max_num ([type]): [description]
+
+    Returns:
+        [type]: [description]
+    """
+
+    actual_num = torch.unsqueeze(actual_num, axis + 1)
+    # tiled_actual_num: [N, M, 1]
+    max_num_shape = [1] * len(actual_num.shape)
+    max_num_shape[axis + 1] = -1
+    max_num = torch.arange(
+        max_num, dtype=torch.int, device=actual_num.device).view(max_num_shape)
+    # tiled_actual_num: [[3,3,3,3,3], [4,4,4,4,4], [2,2,2,2,2]]
+    # tiled_max_num: [[0,1,2,3,4], [0,1,2,3,4], [0,1,2,3,4]]
+    paddings_indicator = actual_num.int() > max_num
+    # paddings_indicator shape: [batch_size, max_num]
+    return paddings_indicator
diff --git a/second/utils/eval.py b/second/utils/eval.py
index bdf6265a..49c7421d 100644
--- a/second/utils/eval.py
+++ b/second/utils/eval.py
@@ -788,12 +788,12 @@ def get_official_eval_result_v1(gt_annos, dt_annos, current_class):
     return result
 
 
-def get_official_eval_result(gt_annos, dt_annos, current_classes, difficultys=[0, 1, 2]):
-    overlap_0_7 = np.array([[0.7, 0.5, 0.5, 0.7,
-                             0.5, 0.7, 0.7, 0.7], [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],
+def get_official_eval_result(gt_annos, dt_annos, current_classes, difficultys=[0, 1, 2], return_data=False):
+    overlap_0_7 = np.array([[0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],
+                            [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],
                             [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7]])
-    overlap_0_5 = np.array([[0.7, 0.5, 0.5, 0.7,
-                             0.5, 0.5, 0.5, 0.5], [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5],
+    overlap_0_5 = np.array([[0.7, 0.5, 0.5, 0.7, 0.5, 0.5, 0.5, 0.5],
+                            [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5],
                             [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5]])
     min_overlaps = np.stack([overlap_0_7, overlap_0_5], axis=0)  # [2, 3, 5]
     class_to_name = {
@@ -847,8 +847,11 @@ def get_official_eval_result(gt_annos, dt_annos, current_classes, difficultys=[0
                 result += print_str((f"aos  AP:{mAPaos[j, 0, i]:.2f}, "
                                      f"{mAPaos[j, 1, i]:.2f}, "
                                      f"{mAPaos[j, 2, i]:.2f}"))
+    if return_data:
+        return result, mAPbbox, mAPbev, mAP3d, mAPaos
+    else:
+        return result
 
-    return result
 
 def get_coco_eval_result(gt_annos, dt_annos, current_classes):
     class_to_name = {