Update README.md

README.md

@@ -35,17 +35,17 @@ The species list is derived from the Collins bird guide [^1].
 import birder
 from birder.inference.classification import infer_image
 
-(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)
+(net, model_info) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)
 
 # Get the image size the model was trained on
-size = birder.get_size_from_signature(signature)
+size = birder.get_size_from_signature(model_info.signature)
 
 # Create an inference transform
-transform = birder.classification_transform(size, rgb_stats)
+transform = birder.classification_transform(size, model_info.rgb_stats)
 
 image = "path/to/image.jpeg"  # or a PIL image, must be loaded in RGB format
 (out, _) = infer_image(net, image, transform)
-# out is a NumPy array with shape of (1, num_classes), representing class probabilities.
+# out is a NumPy array with shape of (1, 707), representing class probabilities.
 ```
 
 ### Image Embeddings
@@ -54,17 +54,17 @@ image = "path/to/image.jpeg"  # or a PIL image, must be loaded in RGB format
 import birder
 from birder.inference.classification import infer_image
 
-(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)
+(net, model_info) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)
 
 # Get the image size the model was trained on
-size = birder.get_size_from_signature(signature)
+size = birder.get_size_from_signature(model_info.signature)
 
 # Create an inference transform
-transform = birder.classification_transform(size, rgb_stats)
+transform = birder.classification_transform(size, model_info.rgb_stats)
 
 image = "path/to/image.jpeg"  # or a PIL image
 (out, embedding) = infer_image(net, image, transform, return_embedding=True)
-# embedding is a NumPy array with shape of (1, embedding_size)
+# embedding is a NumPy array with shape of (1, 768)
 ```
 
 ### Detection Feature Map
@@ -73,13 +73,13 @@ image = "path/to/image.jpeg"  # or a PIL image
 from PIL import Image
 import birder
 
-(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)
+(net, model_info) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-eu-common", inference=True)
 
 # Get the image size the model was trained on
-size = birder.get_size_from_signature(signature)
+size = birder.get_size_from_signature(model_info.signature)
 
 # Create an inference transform
-transform = birder.classification_transform(size, rgb_stats)
+transform = birder.classification_transform(size, model_info.rgb_stats)
 
 image = Image.open("path/to/image.jpeg")
 features = net.detection_features(transform(image).unsqueeze(0))
@@ -96,12 +96,12 @@ print([(k, v.size()) for k, v in features.items()])
 
 ```bibtex
 @misc{woo2023convnextv2codesigningscaling,
-      title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, 
+      title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
       author={Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In So Kweon and Saining Xie},
       year={2023},
       eprint={2301.00808},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2301.00808}, 
+      url={https://arxiv.org/abs/2301.00808},
 }
 ```