moved the notebook to subfolder

README.md

@@ -1,6 +1,6 @@
 # CLIP
 
-[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/Interacting_with_CLIP.ipynb)
+[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb)
 
 CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision.
 

model.py

@@ -329,11 +329,11 @@ class CLIP(nn.Module):
 
         # cosine similarity as logits
         logit_scale = self.logit_scale.exp()
-        logits_per_iamge = logit_scale * image_features @ text_features.t()
+        logits_per_image = logit_scale * image_features @ text_features.t()
         logits_per_text = logit_scale * text_features @ image_features.t()
 
         # shape = [global_batch_size, global_batch_size]
-        return logits_per_iamge, logits_per_text
+        return logits_per_image, logits_per_text
 
 
 def convert_weights(model: nn.Module):