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🚀 CLIP-EBC

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The official implementation of CLIP-EBC, proposed in the paper CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification.

At the release page, you can find weights of the models. For the recent updated CLIP-EBC (ViT-B/16) model, we also provide the training logs (both text and tensorboard files).

Results on NWPU Test

Methods MAE RMSE
DMCount-EBC (based on VGG-19) 83.7 376.0
CLIP-EBC (based on ResNet50) 75.8 367.3
CLIP-EBC (based on ViT-B/16) 61.2 278.3

Visualization

Visualization

Citation

If you find this work useful, please consider to cite:

  • BibTex: 
    @article{ma2024clip,
title={CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification},
author={Ma, Yiming and Sanchez, Victor and Guha, Tanaya},
journal={arXiv preprint arXiv:2403.09281},
year={2024}
}
  • MLA: Ma, Yiming, Victor Sanchez, and Tanaya Guha. "CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification." arXiv preprint arXiv:2403.09281 (2024).
  • APA: Ma, Y., Sanchez, V., & Guha, T. (2024). CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification. arXiv preprint arXiv:2403.09281.

Usage

1. Preprocessing

1.0 Requirements

conda create -n clip_ebc python=3.12.4  # Create a new conda environment. You may use `mamba` instead of `conda` to speed up the installation.
conda activate clip_ebc  # Activate the environment.
pip install -r requirements.txt  # Install the required packages.

1.1 Downloading the datasets

Download all datasets and unzipped them into the folder data.

The data folder should look like:

data:
├─── ShanghaiTech
│   ├── part_A
│   │   ├── train_data
│   │   │   ├── images
│   │   │   └── ground-truth
│   │   └── test_data
│   │       ├── images
│   │       └── ground-truth
│   └── part_B
│       ├── train_data
│       │   ├── images
│       │   └── ground-truth
│       └── test_data
│           ├── images
│           └── ground-truth
├─── NWPU-Crowd
│   ├── images_part1
│   ├── images_part2
│   ├── images_part3
│   ├── images_part4
│   ├── images_part5
│   ├── mats
│   ├── train.txt
│   ├── val.txt
│   └── test.txt
└─── UCF-QNRF
    ├── Train
    └── Test

1.2 Running the preprocessing script

Then, run bash preprocess.sh to preprocess the datasets. In this script, do NOT modify the --dst_dir argument, as the pre-defined paths are used in other files.

2. Training

To train a model, use trainer.py. Below is the script that we used. You can modify the script to train on different datasets and models.

#!/bin/sh
export CUDA_VISIBLE_DEVICES=0  # Set the GPU ID. Comment this line to use all available GPUs.

### Some notes:
# 1. The training script will automatically use all available GPUs in the DDP mode.
# 2. You can use the `--amp` argument to enable automatic mixed precision training to speed up the training process. Could be useful for UCF-QNRF and NWPU.
# 3. Valid values for `--dataset` are `nwpu`, `sha`, `shb`, and `qnrf`.
# See the `trainer.py` for more details.

# Train the commonly used VGG19-based encoder-decoder model on NWPU-Crowd.
python trainer.py \
    --model vgg19_ae --input_size 448 --reduction 8 --truncation 4 --anchor_points average \
    --dataset nwpu \
    --count_loss dmcount &&

# Train the CLIP-EBC (ResNet50) model on ShanghaiTech A. Use `--dataset shb` if you want to train on ShanghaiTech B.
python trainer.py \
    --model clip_resnet50 --input_size 448 --reduction 8 --truncation 4 --anchor_points average --prompt_type word \
    --dataset sha \
    --count_loss dmcount &&

# Train the CLIP-EBC (ViT-B/16) model on UCF-QNRF, using VPT in training and sliding window prediction in testing.
# By default, 32 tokens for each layer are used in VPT. You can also set `--num_vpt` to change the number of tokens.
# By default, the deep visual prompt tuning is used. You can set `--shallow_vpt` to use the shallow visual prompt tuning.
python trainer.py \
    --model clip_vit_b_16 --input_size 224 --reduction 8 --truncation 4 \
    --dataset qnrf --batch_size 16 --amp \
    --num_crops 2 --sliding_window --window_size 224 --stride 224 --warmup_lr 1e-3 \
    --count_loss dmcount

Some Tips

  • DDP: If you don't limit the number of devices, then all GPUs will be used and the code will run in a ddp style.
  • AMP: Simply provide the --amp argument to enable automatic mixed precision training. This could significantly speed up the training on UCF-QNRF and NWPU.

All available models

  • CLIP-based: clip_resnet50, clip_resnet50x4, clip_resnet50x16, clip_resnet50x64, clip_resnet101, clip_vit_b_16, clip_vit_b_32, vit_l_14.
  • Encoder-Decoder:
    • vgg11_ae, vgg11_bn_ae, vgg13_ae, vgg13_bn_ae, vgg16_ae, vgg16_bn_ae, vgg19_ae (the model used in DMCount & BL), vgg19_bn_ae;
    • resnet18_ae, resnet34_ae, resnet50_ae, resnet101_ae, resnet152_ae;
    • csrnet, csrnet_bn;
    • cannet, cannet_bn.
  • Encoder:
    • vit_b_16, vit_b_32, vit_l_16, vit_l_32, vit_h_14;
    • vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn;
    • All timm models that support features_only, out_indices and contain the feature_info attribute.

Arguments in trainer.py

Arguments for models
  • model: which model to train. See all available models above.
  • input_size: the crop size during training.
  • reduction: the reduction factor of the model. This controls the size of the output probability/density map.
  • regression: use blockwise regression instead of classification.
  • truncation: parameter controlling label correction. Currently supported values:
    • configs/reduction_8.json: 2 (all datasets), 4 (all datasets), 11 (only UCF-QNRF).
    • configs/reduction_16.json: 16 (only UCF-QNRF).
    • configs/reduction_19.json: 19 (only UCF-QNRF).
  • anchor_points: the representative count values in the paper. Set average to use the mean count value of the bin. Set middle to use the middle point of the bin.
  • granularity: the granularity of the bins. Choose from "fine", "dynamic", "coarse".
Arguments for CLIP-based models
  • prompt_type: how to represent the count value in the prompt (e.g., if "word", then a prompt could be "There are five people"). Only supported for CLIP-based models.
  • num_vpt: the number of visual prompt tokens. Only supported for ViT-based CLIP-EBC models.
  • vpt_drop: the dropout rate for the visual prompt tokens. Only supported for ViT-based CLIP-EBC models.
  • shallow_vpt: use shallow visual prompt tuning or not. Only supported for ViT-based CLIP-EBC models. The default version is the deep visual prompt tuning.
Arguments for data
  • dataset: which dataset to train on. Choose from "sha", "shb", "nwpu", "qnrf".
  • batch_size: the batch size for training.
  • num_crops: the number of crops generated from each image.
  • min_scale & max_scale: the range of the scale augmentation. We first randomly generate a scale factor from [min_scale, max_scale], then crop the image of the size input_size * scale and then resize it to input_size. This augmentation is used to increase the sample size for large local count values.
  • brightness, contrast, saturation & hue: the parameters for the color jittering augmentation. Note that hue is set to 0.0 by default, as we found using positive values leads to NaN DMCount loss.
  • kernel_size: The kernel size of the Gaussian blur of the cropped image.
  • saltiness & spiciness: parameters for the salt-and-pepper noise augmentation.
  • jitter_prob, blur_prob, noise_prob: the probabilities of the jittering, Gaussian blur, and salt-and-pepper noise augmentations.
Arguments for evaluation
  • sliding_window: use the sliding window prediction method or not in evaluation. Could be useful for transformer-based models.
  • window_size: the size of the sliding window.
  • stride: the stride of the sliding window.
  • strategy: how to handle overlapping regions. Choose from "average" and "max".
  • resize_to_multiple: resize the image to the nearest multiple of window_size before sliding window prediction.
  • zero_pad_to_multiple: zero-pad the image to the nearest multiple of window_size before sliding window prediction.

Note: When using sliding window prediction, if the image size is not a multiple of the window size, then the last stride will be smaller than stride to produce a complete window.

Arguments for training
  • weight_count_loss: the weight of the count loss (e.g. DMCount loss) in the total loss.
  • count_loss: the count loss to use. Choose from "dmcount", "mae", "mse".
  • lr: the maximum learning rate, default to 1e-4.
  • weight_decay: the weight decay, default to 1e-4.
  • warmup_lr: the learning rate for the warm-up period, default to 1e-6.
  • warmup_epochs: the number of warm-up steps, default to 50.
  • T_0, T_mult, eta_min: the parameters for CosineAnnealingWarmRestarts scheduler. The learning rate will increase from warmup_lr to lr during the first warmup_epochs epochs, then adjusted by the cosine annealing schedule.
  • total_epochs: the total number of epochs to train.
  • eval_start: the epoch to start evaluation.
  • eval_freq: the frequency of evaluation.
  • save_freq: the frequency of saving the model. Could be useful to reduce I/O.
  • save_best_k: save the best k models based on the evaluation metric.
  • amp: use automatic mixed precision training or not.
  • num_workers: the number of workers for data loading.
  • local_rank: do not set this argument. It is used for multi-GPU training.
  • seed: the random seed, default to 42.

3. Testing on NWPU Test

To evaluate get the result on NWPU Test, use the test_nwpu.py instead.

# Test CNN-based models
python test_nwpu.py \
    --model vgg19_ae --input_size 448 --reduction 8 --truncation 4 --anchor_points average \
    --weight_path ./checkpoints/nwpu/vgg19_ae_448_8_4_fine_1.0_dmcount_aug/best_mae.pth
    --device cuda:0 &&

# Test ViT-based models. Need to use the sliding window prediction method.
python test_nwpu.py \
    --model clip_vit_b_16 --input_size 224 --reduction 8 --truncation 4 --anchor_points average --prompt_type word \
    --num_vpt 32 --vpt_drop 0.0 --sliding_window --stride 224 \
    --weight_path ./checkpoints/nwpu/clip_vit_b_16_word_224_8_4_fine_1.0_dmcount/best_rmse.pth
    --device cuda:0

4. Visualization

Use the model.ipynb notebook to visualize the model predictions.