MSRVTT Data

Frozen️ in Time ❄️️️️️⏳

A Joint Video and Image Encoder for End-to-End Retrieval

project page | paper | dataset | demo Repository containing the code, models, data for end-to-end retrieval. WebVid data can be found here

📝 Preparation

Create conda env conda env create
Create data / experiment folders mkdir data; mkdir exps, note this can just be a symlink to where you want to store big data.

🔧 Finetuning (benchmarks: MSR-VTT)

wget https://www.robots.ox.ac.uk/~maxbain/frozen-in-time/data/MSRVTT.zip -P data; unzip data/MSRVTT.zip -d data
Change num_gpus in the config file accordingly.
Train python train.py --config configs/msrvtt_4f_i21k.json
Test python test.py --resume exps/models/{EXP_NAME}/{EXP_TIMESTAMP}/model_best.pth

For finetuning a pretrained model, set "load_checkpoint": "PATH_TO_MODEL" in the config file.

🏋️‍️ Pretraining

Download WebVid-2M (see https://github.com/m-bain/webvid)
Download CC-3M (see https://ai.google.com/research/ConceptualCaptions/download)
Train. python train.py --config CONFIG_PATH. Here are the different options:

a. Dataset combinations
```
 i. CC-3M + WebVid2M: configs/cc-webvid2m-pt-i2k.json
 ii. WebVid2M : configs/webvid2m-pt-i2k.json
```
You can add in an arbitrary number of image/video datasets for pre-training by adding as many dataloaders to the config file dataloader list as your heart desires. Adding more datasets will likely to higher downstream performance.

b. Number of frames

For image datasets, this should always be set to video_params": {"num_frames": 1, ...}.

For video datasets, set this to what you want. N.B. More frames requires = more gpu memory.

If, like us, you are not a big company and have limited compute, then you will benefit by training via a curriculum on the number of frames. A lot of the knowledge can be learned in the 1-frame setting, as we show in the paper. You can then finetune with more frames. See curriculum learning section

c. Finetuning

Set "load_checkpoint": "FULL_MODEL_PATH" in the config file. You can now use different experiment params, such as num_frames, to do curriculum learning for example.

🗄 Pretrained Weights

CC-3M+WebVid-2M, 4-frames, base_patch_16_224

📚 Curriculum Learning on #frames

Curriculum learning on the number of frames in pretraining achieves similar performance with significant reduction in compute (both memory and training time). This is because model has higher throughput for fewer frames, as well as allowing a bigger batch size for the same gpu memory.

Our best model was trained on 1-frame then finetuned on 4-frames on CC+WebVid2M.

Train on 1-frame until the training loss converges, then finetune on 4-frames with the same config, from the 1-frame checkpoint via setting load_checkpoint in config file. 4-frame finetuning needs much less iterations (~10% of 1-frame setting is sufficient) since most of the knowledge is learned in the 1-frame setting.

📈 Experiment Logging and Visualising

This repository uses a sacred backbone for logging and tracking experiments, with a neptune front end. It makes life a lot easier. If you want to activate this:

Create a neptune.ai account.
Create a project, copy in your credentials in train.py and remove the ValueError
Set neptune: true in your config files.

🔍 Creating a semantic visual search engine

This repository can be used to extract visual features using the frozen in time model in order to create a text to visual semantic search engine, such as our demo. This uses the FAISS library for rapid indexing of millions of features vectors. Follow the instructions in index_search.md.

🎓 Cite

If you use this code in your research, please cite:

@InProceedings{Bain21,
  author       = "Max Bain and Arsha Nagrani and G{\"u}l Varol and Andrew Zisserman",
  title        = "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval",
  booktitle    = "IEEE International Conference on Computer Vision",
  year         = "2021",
}

LICENSE

This project is licensed under the MIT License. See LICENSE for more details

🙏 Acknowledgements

This code is based off the pytorch-template https://github.com/victoresque/pytorch-template

As well as many good practices adopted from Samuel Albanie's https://github.com/albanie/collaborative-experts

	'val': transforms.Compose([
	transforms.Resize(center_crop),
	transforms.CenterCrop(center_crop),
	transforms.Resize(input_res),
	normalize,
	]),

	intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int)
	ranges = []
	for idx, interv in enumerate(intervals[:-1]):
	ranges.append((interv, intervals[idx + 1] - 1))

	frames = torch.stack([frames[idx] for idx in frame_idxs]).float() / 255
	frames = frames.permute(0, 3, 1, 2)
	return frames, frame_idxs

	frames = torch.stack(frames).float() / 255
	cap.release()
	return frames, success_idxs

m-bain / frozen-in-time Goto Github PK

frozen-in-time's Introduction

Frozen️ in Time ❄️️️️️⏳

A Joint Video and Image Encoder for End-to-End Retrieval

📝 Preparation

🔧 Finetuning (benchmarks: MSR-VTT)

🏋️‍️ Pretraining

🗄 Pretrained Weights

📚 Curriculum Learning on #frames

📈 Experiment Logging and Visualising

🔍 Creating a semantic visual search engine

🎓 Cite

LICENSE

🙏 Acknowledgements

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