### Install CLIP Dependencies

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Installs necessary packages for CLIP, including PyTorch, torchvision, and other Python libraries. Ensure you have the correct CUDA toolkit version installed.

```sh
conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
pip install ftfy regex tqdm
pip install opencv-python boto3 requests pandas
```

--------------------------------

### Train CLIP4Clip on DiDeMo Dataset

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Command for training CLIP4Clip on DiDeMo for video-paragraph retrieval. This task also requires a significant number of GPUs.

```sh
DATA_PATH=[Your DiDeMo data and videos path]
python -m torch.distributed.launch --nproc_per_node=8 \
main_task_retrieval.py --do_train --num_thread_reader=2 \
--epochs=5 --batch_size=128 --n_display=50 \
--data_path ${DATA_PATH} \
--features_path ${DATA_PATH}/DiDeMo_Videos \
--output_dir ckpts/ckpt_didemo_retrieval_looseType \
--lr 1e-4 --max_words 64 --max_frames 64 --batch_size_val 16 \
--datatype didemo --feature_framerate 1 --coef_lr 1e-3 \
--freeze_layer_num 0  --slice_framepos 2 \
--loose_type --linear_patch 2d --sim_header meanP \
--pretrained_clip_name ViT-B/32
```

--------------------------------

### Download CLIP (ViT-B/32) Weight

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Downloads the pre-trained weights for the ViT-B/32 CLIP model. Save these weights in the './modules' directory.

```sh
wget -P ./modules https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt
```

--------------------------------

### Train CLIP4Clip on ActivityNet Dataset

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

This snippet trains CLIP4Clip for video-paragraph retrieval on ActivityNet. Requires more GPUs due to the nature of the task.

```sh
DATA_PATH=[Your ActivityNet data and videos path]
python -m torch.distributed.launch --nproc_per_node=8 \
main_task_retrieval.py --do_train --num_thread_reader=2 \
--epochs=5 --batch_size=128 --n_display=50 \
--data_path ${DATA_PATH} \
--features_path ${DATA_PATH}/Activity_Videos \
--output_dir ckpts/ckpt_activity_retrieval_looseType \
--lr 1e-4 --max_words 64 --max_frames 64 --batch_size_val 16 \
--datatype activity --feature_framerate 1 --coef_lr 1e-3 \
--freeze_layer_num 0  --slice_framepos 2 \
--loose_type --linear_patch 2d --sim_header meanP \
--pretrained_clip_name ViT-B/32
```

--------------------------------

### Train CLIP4Clip on LSMDC Dataset

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Command to initiate CLIP4Clip training for retrieval on the LSMDC dataset. Ensure DATA_PATH points to your LSMDC data.

```sh
DATA_PATH=[Your LSMDC data and videos path]
python -m torch.distributed.launch --nproc_per_node=4 \
main_task_retrieval.py --do_train --num_thread_reader=2 \
--epochs=5 --batch_size=128 --n_display=50 \
--data_path ${DATA_PATH} \
--features_path ${DATA_PATH}/LSMDC_Videos \
--output_dir ckpts/ckpt_lsmdc_retrieval_looseType \
--lr 1e-4 --max_words 32 --max_frames 12 --batch_size_val 16 \
--datatype lsmdc --feature_framerate 1 --coef_lr 1e-3 \
--freeze_layer_num 0  --slice_framepos 2 \
--loose_type --linear_patch 2d --sim_header meanP \
--pretrained_clip_name ViT-B/32
```

--------------------------------

### Download CLIP (ViT-B/16) Weight

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Downloads the pre-trained weights for the ViT-B/16 CLIP model. Save these weights in the './modules' directory. This model offers better performance.

```sh
wget -P ./modules https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt
```

--------------------------------

### Download MSVD Data

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Downloads the MSVD dataset splits and raw captions. This script provides convenient access to the dataset files.

```sh
wget https://github.com/ArrowLuo/CLIP4Clip/releases/download/v0.0/msvd_data.zip
```

--------------------------------

### Train CLIP4Clip on MSRVTT Dataset

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Use this command to train the CLIP4Clip model for retrieval on the MSRVTT dataset. Ensure the DATA_PATH is correctly set.

```sh
DATA_PATH=[Your MSRVTT data and videos path]
python -m torch.distributed.launch --nproc_per_node=4 \
main_task_retrieval.py --do_train --num_thread_reader=0 \
--epochs=5 --batch_size=128 --n_display=50 \
--train_csv ${DATA_PATH}/MSRVTT_train.9k.csv \
--val_csv ${DATA_PATH}/MSRVTT_JSFUSION_test.csv \
--data_path ${DATA_PATH}/MSRVTT_data.json \
--features_path ${DATA_PATH}/MSRVTT_Videos \
--output_dir ckpts/ckpt_msrvtt_retrieval_looseType \
--lr 1e-4 --max_words 32 --max_frames 12 --batch_size_val 16 \
--datatype msrvtt --expand_msrvtt_sentences  \
--feature_framerate 1 --coef_lr 1e-3 \
--freeze_layer_num 0  --slice_framepos 2 \
--loose_type --linear_patch 2d --sim_header meanP \
--pretrained_clip_name ViT-B/32
```

--------------------------------

### Train CLIP4Clip on MSVD Dataset

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

This command trains the CLIP4Clip model for retrieval on the MSVD dataset. Adjust DATA_PATH to your local directory.

```sh
DATA_PATH=[Your MSVD data and videos path]
python -m torch.distributed.launch --nproc_per_node=4 \
main_task_retrieval.py --do_train --num_thread_reader=2 \
--epochs=5 --batch_size=128 --n_display=50 \
--data_path ${DATA_PATH} \
--features_path ${DATA_PATH}/MSVD_Videos \
--output_dir ckpts/ckpt_msvd_retrieval_looseType \
--lr 1e-4 --max_words 32 --max_frames 12 --batch_size_val 16 \
--datatype msvd \
--feature_framerate 1 --coef_lr 1e-3 \
--freeze_layer_num 0 --slice_framepos 2 \
--loose_type --linear_patch 2d --sim_header meanP \
--pretrained_clip_name ViT-B/32
```

--------------------------------

### Compress Video for Speed-up

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Compresses videos to a specified frame rate (3fps) and width (224). Modify input_root and output_root paths as needed. This script helps optimize video processing speed.

```sh
python preprocess/compress_video.py --input_root [raw_video_path] --output_root [compressed_video_path]
```

--------------------------------

### Download MSRVTT Data

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Downloads the MSRVTT dataset splits and captions. This is a convenience script for accessing the data.

```sh
wget https://github.com/ArrowLuo/CLIP4Clip/releases/download/v0.0/msrvtt_data.zip
```

--------------------------------

### Download MSRVTT Raw Videos

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

Downloads raw videos for the MSRVTT dataset from the 'Frozen in Time' repository. This is an alternative source for video data.

```sh
wget https://www.robots.ox.ac.uk/~maxbain/frozen-in-time/data/MSRVTT.zip
```

--------------------------------

### CLIP4Clip Citation

Source: https://github.com/arrowluo/clip4clip/blob/master/README.md

If you find CLIP4Clip useful in your work, please cite the following paper.

```bibtex
@Article{Luo2021CLIP4Clip,
  author  = {Huaishao Luo and Lei Ji and Ming Zhong and Yang Chen and Wen Lei and Nan Duan and Tianrui Li},
  title   = {{CLIP4Clip}: An Empirical Study of CLIP for End to End Video Clip Retrieval},
  journal = {arXiv preprint arXiv:2104.08860},
  year    = {2021},
}
```

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