nielsr HF staff fcakyon commited on
Commit
eed8700
1 Parent(s): 113f762

fix a typo in code snippet (#2)

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- fix a typo in code snippet (3753aeee87a9b3b11c8e4fcff74f0c824a4cafed)
- Update README.md (048d3c90f3f8c5343255801578a89ee6e1caad71)


Co-authored-by: Fatih <fcakyon@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +4 -4
README.md CHANGED
@@ -20,16 +20,16 @@ You can use the raw model for video classification into one of the 174 possible
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  Here is how to use this model to classify a video:
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  ```python
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- from transformers import TimesformerFeatureExtractor, TimesformerForVideoClassification
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  import numpy as np
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  import torch
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  video = list(np.random.randn(16, 3, 448, 448))
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- feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-hr-finetuned-ssv2")
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- model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-ssv22")
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- inputs = feature_extractor(video, return_tensors="pt")
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  with torch.no_grad():
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  outputs = model(**inputs)
 
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  Here is how to use this model to classify a video:
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  ```python
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+ from transformers import AutoImageProcessor, TimesformerForVideoClassification
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  import numpy as np
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  import torch
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  video = list(np.random.randn(16, 3, 448, 448))
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+ processor = AutoImageProcessor.from_pretrained("facebook/timesformer-hr-finetuned-ssv2")
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+ model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-ssv2")
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+ inputs = feature_extractor(images=video, return_tensors="pt")
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  with torch.no_grad():
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  outputs = model(**inputs)