Official PyTorch Implementation of Few-shot Object Counting with Similarity-Aware Feature Enhancement, Accepted by WACV 2023.

• Create the FSC147 dataset directory. Download the FSC147 dataset from here. Unzip the file and move some to ./data/FSC147_384_V2/. The FSC147 dataset directory should be as follows.

|-- data
    |-- FSC147_384_V2
        |-- images_384_VarV2
        |-- gt_density_map_adaptive_384_VarV2
        |-- train.json
        |-- val.json
        |-- test.json
        |-- fold0.json
        |-- fold1.json
        |-- fold2.json
        |-- fold3.json

• cd the experiment directory by running cd ./experiments/FSC147/.

• Train, eval, or test by running:

  (1) For slurm group: sh train.sh #NUM_GPUS #PARTITION, sh eval.sh #NUM_GPUS #PARTITION, or sh test.sh #NUM_GPUS #PARTITION.

  (2) For torch.distributed.launch: sh train_torch.sh #NUM_GPUS #GPU_IDS, sh eval_torch.sh #NUM_GPUS #GPU_IDS, or sh test_torch.sh #NUM_GPUS #GPU_IDS, e.g., train with GPU 1,3,4,6 (4 GPU in total): sh train_torch.sh 4 1,3,4,6.

  Note: During eval or test, please set config.saver.load_path to load the checkpoints.

• Results and checkpoints.

Here we provide one example (3 shot, fold0 as val set), and others are similar.

• Create the FSC147 dataset directory, the same as in 1.1.

• cd the experiment directory by running cd experiments/FSC147_fold/3shot/fold0/.

• Train or eval by running:

  (1) For slurm group: sh train.sh #NUM_GPUS #PARTITION or sh eval.sh #NUM_GPUS #PARTITION.

  (2) For torch.distributed.launch: sh train_torch.sh #NUM_GPUS #GPU_IDS or sh eval_torch.sh #NUM_GPUS #GPU_IDS.

  Note: During eval or test, please set config.saver.load_path to load the checkpoints.

• Results. Training on 8 GPUs (NVIDIA Tesla V100 16GB) results in following performance.

• Create the FSC147 & CARPK dataset directory, the same as in 1.1 & 1.4.

• cd the experiment directory by running cd experiments/FSC147_to_CARPK/.

• Pretrain, finetune, or eval. The pretrain, finetune, or eval are similar to FSC147.

• Results. Training on 8 GPUs (NVIDIA Tesla V100 16GB) results in following performance.

The train and eval of class-specific counting are similar to FSC147. Here we only provide the construction of the dataset directory. The checkpoints (trained on 8 NVIDIA Tesla V100 16GB GPUs) and the corresponding results are given.

1) CARPK

• Download the CARPK dataset from here. Unzip the file and move some to ./data/CARPK_devkit/. The CARPK dataset directory should be as follows.

|-- data
    |-- CARPK_devkit
        |-- Images
        |-- gen_gt_density.py
        |-- train.json
        |-- test.json
        |-- exemplar.json

• run python gen_gt_density.py to generate ground-truth density map. The ground-truth density map will be saved to ./data/CARPK_devkit/gt_density_map/.

2) PUCPR

• Download the PUCPR dataset from here. Unzip the file and move some to ./data/PUCPR _devkit/. The PUCPR dataset directory should be as follows.

|-- data
    |-- PUCPR _devkit
        |-- Images
        |-- gen_gt_density.py
        |-- train.json
        |-- test.json
        |-- exemplar.json

• run python gen_gt_density.py to generate ground-truth density map. The ground-truth density map will be saved to ./data/PUCPR _devkit/gt_density_map/.

3) UCSD

• Download the UCSD dataset from here. Unzip the file and move some to ./data/UCSD/. The UCSD dataset directory should be as follows.

|-- data
    |-- UCSD
        |-- ucsdpeds_vidf
        |-- gen_gt_density.py
        |-- train.json
        |-- test.json
        |-- exemplar.json
        |-- mask.png

• We use the annotations in vidf-cvpr.zip, which corresponds to 10 directories (from ucsdpeds_vidf/video/vidf/vidf1_33_000.y/ to ucsdpeds_vidf/video/vidf/vidf1_33_009.y/). Merge all images under these 10 directories to ucsdpeds_vidf/video/vidf/. Other directories could be removed.

• run python gen_gt_density.py to generate ground-truth density map. The ground-truth density map will be saved to ./data/UCSD/gt_density_map/.

4) Mall

• Download the Mall dataset from here. Unzip the file and move some to ./data/Mall/. The Mall dataset directory should be as follows.

|-- data
    |-- Mall
        |-- frames
        |-- gen_gt_density.py
        |-- train.json
        |-- test.json
        |-- exemplar.json
        |-- mask.png

• run python gen_gt_density.py to generate ground-truth density map. The ground-truth density map will be saved to ./data/Mall/gt_density_map/.

5) ShanghaiTech

• Download the ShanghaiTech dataset from here. Unzip the file and move some to ./data/ShanghaiTech/. The ShanghaiTech dataset directory should be as follows.

|-- data
    |-- ShanghaiTech
        |-- part_A
            |-- train_data
                |-- images
            |-- test_data
                |-- images
            |-- gen_gt_density.py
            |-- train.json
            |-- test.json
            |-- exemplar.json
        |-- part_B
            |-- train_data
                |-- images
            |-- test_data
                |-- images
            |-- gen_gt_density.py
            |-- train.json
            |-- test.json
            |-- exemplar.json

• run python gen_gt_density.py to generate ground-truth density map. Note that you should run this twice for both part_A and part_B. The ground-truth density map will be saved to (1) ./data/ShanghaiTech/part_A/train_data/gt_density_map/, (2) ./data/ShanghaiTech/part_A/test_data/gt_density_map/, (3) ./data/ShanghaiTech/part_B/train_data/gt_density_map/, (4) ./data/ShanghaiTech/part_B/test_data/gt_density_map/.

• We provide two datasets: custom_dataset.py & custom_exemplar_dataset.py, and three models: safecount.py, safecount_exemplar.py, & safecount_crossdataset.py. They should be cooperated and used as follows.

• CUDA Out of Memory.

  (1). Choose a smaller image size (config.dataset.input_size).

  (2). Set a smaller exemplar number (config.dataset.shot for FSC147, config.dataset.exemplar.num_exemplar for class-specific counting).

  (3). Set a larger out_stride (config.net.kwargs.backbone.out_stride), but you also need to revise the Regressor (in models.utils.py) to upsample the feature to the original image size.