76 lines
2.9 KiB
Markdown
76 lines
2.9 KiB
Markdown
---
|
|
language: en
|
|
license: mit
|
|
tags:
|
|
- vision
|
|
- video-classification
|
|
model-index:
|
|
- name: nielsr/xclip-base-patch16-zero-shot
|
|
results:
|
|
- task:
|
|
type: video-classification
|
|
dataset:
|
|
name: HMDB-51
|
|
type: hmdb-51
|
|
metrics:
|
|
- type: top-1 accuracy
|
|
value: 44.6
|
|
- task:
|
|
type: video-classification
|
|
dataset:
|
|
name: UCF101
|
|
type: ucf101
|
|
metrics:
|
|
- type: top-1 accuracy
|
|
value: 72.0
|
|
- task:
|
|
type: video-classification
|
|
dataset:
|
|
name: Kinetics-600
|
|
type: kinetics600
|
|
metrics:
|
|
- type: top-1 accuracy
|
|
value: 65.2
|
|
---
|
|
|
|
# X-CLIP (base-sized model)
|
|
|
|
X-CLIP model (base-sized, patch resolution of 16) trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
|
|
|
|
This model was trained using 32 frames per video, at a resolution of 224x224.
|
|
|
|
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
|
|
|
|
## Model description
|
|
|
|
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
|
|
|
|
|
|
|
|
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
|
|
|
|
## Intended uses & limitations
|
|
|
|
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
|
|
fine-tuned versions on a task that interests you.
|
|
|
|
### How to use
|
|
|
|
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
|
|
|
|
## Training data
|
|
|
|
This model was trained on [Kinetics 400](https://www.deepmind.com/open-source/kinetics).
|
|
|
|
### Preprocessing
|
|
|
|
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
|
|
|
|
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
|
|
|
|
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
|
|
|
|
## Evaluation results
|
|
|
|
This model achieves a zero-shot top-1 accuracy of 44.6% on HMDB-51, 72.0% on UCF-101 and 65.2% on Kinetics-600.
|