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timesformer-hr-finetuned-k400
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fix code snippet in model card (#2) 

- fix code snippet in model card (b0d8688ea485cafceaa5233f8018a3d067b29b36)
- Update README.md (0dca5362a3365155b15753a58c70150a55c5d0cd)


Co-authored-by: Fatih <fcakyon@users.noreply.huggingface.co>
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Niels Rogge 2022-12-06 08:16:45 +00:00 committed by system
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@ -20,16 +20,16 @@ You can use the raw model for video classification into one of the 400 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(16, 3, 448, 448))
feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-hr-finetuned-k400") processor = AutoImageProcessor.from_pretrained("facebook/timesformer-hr-finetuned-k400")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-k400") model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-k400")
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)