From c12281dea7770ed473ce6fdb6a47b585fd17dc8c Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Tue, 6 Dec 2022 08:15:59 +0000 Subject: [PATCH] fix a typo in code snippet (#3) - fix a typo in code snippet (4655a3825d3fd6bcdc6f862f92bae4c348ab3516) - Update README.md (fa5f033feb1c19ba739ff3ebbd1447fe85ebf4b0) Co-authored-by: Fatih --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 2bc4395..474803a 100644 --- a/README.md +++ b/README.md @@ -20,16 +20,16 @@ You can use the raw model for video classification into one of the 174 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)) -feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-base-finetuned-ssv2") +processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-ssv2") model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-ssv2") -inputs = feature_extractor(video, return_tensors="pt") +inputs = processor(images=video, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs)