facebook
/
timesformer-base-finetuned-k600
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix code snippet in model card (#3) 

- fix code snippet in model card (18f67186e115131c4df8d89788d8d3356da506aa)
- Update README.md (595e15554a3c0eafb01c367e9ef0016658f7655a)


Co-authored-by: Fatih <fcakyon@users.noreply.huggingface.co>
This commit is contained in:
Niels Rogge 2022-12-06 08:15:11 +00:00 committed by system
parent 3da1913755
commit df9bcb190c
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 600 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-k600") processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-k600")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k600") model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k600")
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)