nielsr HF staff fcakyon commited on
Commit
c080598
1 Parent(s): d88ecd6

fix a typo in code snippet and processor config (#2)

Browse files

- fix a typo in code snippet (d4a091673f1e222362b66e76cd12503485811488)
- Update README.md (283d3dadb4278dff272703e1e49660120ac9ee32)
- Update README.md (3d47cc1abbe7e66e6e1508588b094529329c99a0)
- fix processor config (5c99ed640fbd5953e8c10441c808bbb1d4eedca4)


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

Files changed (2) hide show
  1. README.md +4 -4
  2. preprocessor_config.json +3 -3
README.md CHANGED
@@ -20,16 +20,16 @@ You can use the raw model for video classification into one of the 600 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(8, 3, 224, 224))
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- feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-hr-finetuned-k600")
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  model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-k600")
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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-k600")
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  model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-k600")
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+ inputs = processor(images=video, return_tensors="pt")
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  with torch.no_grad():
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  outputs = model(**inputs)
preprocessor_config.json CHANGED
@@ -1,7 +1,7 @@
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  {
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  "crop_size": {
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- "height": 224,
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- "width": 224
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  },
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  "do_center_crop": true,
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  "do_normalize": true,
@@ -21,6 +21,6 @@
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  "resample": 2,
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  "rescale_factor": 0.00392156862745098,
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  "size": {
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- "shortest_edge": 224
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  }
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  }
 
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  {
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  "crop_size": {
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+ "height": 448,
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+ "width": 448
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  },
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  "do_center_crop": true,
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  "do_normalize": true,
 
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  "resample": 2,
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  "rescale_factor": 0.00392156862745098,
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  "size": {
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+ "shortest_edge": 448
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  }
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  }