From df9bcb190c80bffe2b4a0746ab604835ff876fab Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Tue, 6 Dec 2022 08:15:11 +0000 Subject: [PATCH] fix code snippet in model card (#3) - fix code snippet in model card (18f67186e115131c4df8d89788d8d3356da506aa) - Update README.md (595e15554a3c0eafb01c367e9ef0016658f7655a) Co-authored-by: Fatih --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index ff32726..0fed724 100644 --- a/README.md +++ b/README.md @@ -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)) -feature_extractor = TimesformerFeatureExtractor.from_pretrained("facebook/timesformer-base-finetuned-k600") +processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-k600") model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k600") -inputs = feature_extractor(video, return_tensors="pt") +inputs = processor(images=video, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs)