facebook
/
timesformer-base-finetuned-ssv2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix a typo in code snippet (#3) 

- fix a typo in code snippet (4655a3825d3fd6bcdc6f862f92bae4c348ab3516)
- Update README.md (fa5f033feb1c19ba739ff3ebbd1447fe85ebf4b0)


Co-authored-by: Fatih <fcakyon@users.noreply.huggingface.co>
This commit is contained in:
Niels Rogge 2022-12-06 08:15:59 +00:00 committed by system
parent 53067950fd
commit c12281dea7
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
README.md
View File

@ -20,16 +20,16 @@ You can use the raw model for video classification into one of the 174 possible
Here is how to use this model to classify a video: Here is how to use this model to classify a video:
```python ```python
from transformers import TimesformerFeatureExtractor, TimesformerForVideoClassification from transformers import AutoImageProcessor, TimesformerForVideoClassification
import numpy as np import numpy as np
import torch import torch
video = list(np.random.randn(8, 3, 224, 224)) video = list(np.random.randn(8, 3, 224, 224))
feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-base-finetuned-ssv2") processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-ssv2")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-ssv2") model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-ssv2")
inputs = feature_extractor(video, return_tensors="pt") inputs = processor(images=video, return_tensors="pt")
with torch.no_grad(): with torch.no_grad():
outputs = model(**inputs) outputs = model(**inputs)