Update README.md

README.md

@@ -32,17 +32,17 @@ The species list is derived from data available at <https://www.israbirding.com/
 import birder
 from birder.inference.classification import infer_image
 
-(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("mvit_v2_t_il-all", inference=True)
+(net, model_info) = birder.load_pretrained_model("mvit_v2_t_il-all", 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, 550), representing class probabilities.
 ```
 
 ### Image Embeddings
@@ -51,17 +51,17 @@ 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("mvit_v2_t_il-all", inference=True)
+(net, model_info) = birder.load_pretrained_model("mvit_v2_t_il-all", 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
@@ -70,13 +70,13 @@ 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("mvit_v2_t_il-all", inference=True)
+(net, model_info) = birder.load_pretrained_model("mvit_v2_t_il-all", 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))
@@ -93,12 +93,12 @@ print([(k, v.size()) for k, v in features.items()])
 
 ```bibtex
 @misc{li2022mvitv2improvedmultiscalevision,
-      title={MViTv2: Improved Multiscale Vision Transformers for Classification and Detection}, 
+      title={MViTv2: Improved Multiscale Vision Transformers for Classification and Detection},
       author={Yanghao Li and Chao-Yuan Wu and Haoqi Fan and Karttikeya Mangalam and Bo Xiong and Jitendra Malik and Christoph Feichtenhofer},
       year={2022},
       eprint={2112.01526},
       archivePrefix={arXiv},
       primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2112.01526}, 
+      url={https://arxiv.org/abs/2112.01526},
 }
 ```