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timesformer-hr-finetuned-k600
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fix a typo in code snippet and processor config (#2) 

- 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>
This commit is contained in:
Niels Rogge 2022-12-06 08:16:23 +00:00 committed by system
parent d88ecd6e9a
commit c0805983e9
2 changed files with 7 additions and 7 deletions
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8
README.md
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@ -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:
```python
from transformers import TimesformerFeatureExtractor, TimesformerForVideoClassification
from transformers import AutoImageProcessor, TimesformerForVideoClassification
import numpy as np
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-k600")
processor = AutoImageProcessor.from_pretrained("facebook/timesformer-hr-finetuned-k600")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-hr-finetuned-k600")
inputs = feature_extractor(video, return_tensors="pt")
inputs = processor(images=video, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)

6
preprocessor_config.json
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@ -1,7 +1,7 @@
{
"crop_size": {
"height": 224,
"width": 224
"height": 448,
"width": 448
},
"do_center_crop": true,
"do_normalize": true,
@ -21,6 +21,6 @@
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
"shortest_edge": 448
}
}