Introduction

An unofficial PyTorch reimplementation of SqueezeDet, with better performance on KITTI benchmark than the official Tensorflow version (and cleaner codes!)

A lot of the code has been borrowed from https://github.com/QiuJueqin/SqueezeDet-PyTorch The purpose was to understand Squeeze Det and see how it performs on KITTI For my case I trained the model for 223 epochs approximately due to limited resources(even colab pro has its limitations) Then I used the model trained by QiuJuqin to evaluate the model's performance on KITTI Special thanks to QiuJueqin for open sourcing his work

Performance

KITTI

Classes	Car (E/M/H)	Pedestrian (E/M/H)	Cyclist (E/M/H)	mAP
This repo	92.8 / 88.7 / 79.0	82.1 / 75.4 / 72.5	82.3 / 76.9 / 73.8	80.4
Paper	90.2 / 84.7 / 73.9	82.9 / 75.4 / 72.1	77.1 / 68.3 / 65.8	76.7

Usage

Environment

This project was developed and tested on Colab.

Installation

Clone this repository:

git clone https://github.com/sparshgarg23/SqueezeDet-PyTorch-1

Here ROOT_DIR is simply content/SqueezeDet-Pytorch-1

Run demo

Simply run:

cd $ROOT_DIR/src
python main.py demo

That's it. All pre-trained model and sample images have been included in the repo. If everything goes well you will see detection results like this:

Training & evaluation

KITTI 2D object detection benchmark

Download KITTI object detection dataset (images and labels) to your disk, unzip them and you will get two directories: training/ and testing/. Create a symbolic link to data/kitti of the working directory:
```
mkdir $ROOT_DIR/data/kitti
ln -s training/ testing/ $ROOT_DIR/data/kitti
```

Then read all samples' ID into a trainval.txt file:

cd $ROOT_DIR/data/kitti
mkdir image_sets
cd ./image_sets
ls ../training/image_2/ | grep ".png" | sed s/.png// > trainval.txt

Use the script to randomly split the KITTI's training data into a training set and a vlidation set half-and-half:

cd $ROOT_DIR/src/utils
python random_split_train_val.py

A train.txt and a val.txt files will be generated that record the sample IDs for training and validation sets. Now the structure of your data folder should look like:

$ROOT_DIT/data/kitti/
                  |--training/
                  |     |-- image_2/00****.png
                  |     L-- label_2/00****.txt
                  |--testing/
                  |     L-- image_2/00****.png
                  L--image_sets/
                        |-- trainval.txt
                        |-- train.txt
                        L-- val.txt

Before using KITTI's development kit to evaluate models, we need to compile it first:
```
cd $ROOT_DIR/src/utils/kitti-eval
make
```

To evaluate trained model on the validation set, run:

cd $ROOT_DIR/src
python main.py eval --load_model ../models/squeezedet_kitti_epoch280.pth --exp_id my_eval

and you will get results like this:

Your evaluation results are available in /home/qiujueqin/squeezedet/exp/my_eval/results
Car_easy             0.922
Car_moderate         0.877
Car_hard             0.784
Pedestrian_easy      0.814
Pedestrian_moderate  0.743
Pedestrian_hard      0.718
Cyclist_easy         0.816
Cyclist_moderate     0.762
Cyclist_hard         0.737
mAP                  0.797

To train the model, run:

python main.py train --load_model ../models/imagenet/squeezenet1_1-f364aa15.pth --exp_id my_train

where squeezenet1_1-f364aa15.pth is a PyTorch official SqueezeNet model pre-trained on ImageNet. We do not recommend to train SqueezeDet from scratch. All logs will be saved to $ROOT_DIR/exp/my_train directory.

By default we use batch_size=20 and num_workers=4. You can find more parameter options by running python main.py --help:

usage: main.py [-h] [--dataset DATASET] [--load_model LOAD_MODEL]
             [--debug DEBUG] [--exp_id EXP_ID] [--arch ARCH]
             [--dropout_prob DROPOUT_PROB] [--lr LR] [--momentum MOMENTUM]
             [--weight_decay WEIGHT_DECAY] [--grad_norm GRAD_NORM]
             [--num_epochs NUM_EPOCHS] [--num_iters NUM_ITERS]
             [--batch_size BATCH_SIZE]
             [--master_batch_size MASTER_BATCH_SIZE]
             [--save_intervals SAVE_INTERVALS]
             [--val_intervals VAL_INTERVALS] [--no_eval]
             [--print_interval PRINT_INTERVAL] [--flip_prob FLIP_PROB]
             [--drift_prob DRIFT_PROB] [--forbid_resize]
             [--class_loss_weight CLASS_LOSS_WEIGHT]
             [--positive_score_loss_weight POSITIVE_SCORE_LOSS_WEIGHT]
             [--negative_score_loss_weight NEGATIVE_SCORE_LOSS_WEIGHT]
             [--bbox_loss_weight BBOX_LOSS_WEIGHT] [--nms_thresh NMS_THRESH]
             [--score_thresh SCORE_THRESH] [--keep_top_k KEEP_TOP_K]
             [--gpus GPUS] [--num_workers NUM_WORKERS]
             [--not_cuda_benchmark] [--seed SEED]
             mode
...

Here are loss curve and mAP curve from one training process:

License

Licensed under MIT.

sparshgarg23 / SqueezeDet-PyTorch-1