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swin_transformer_v2_s_intermediate-arabian-peninsula

Image Classification
Birder
PyTorch
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Update README.md

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  1. README.md +11 -11
README.md CHANGED
@@ -33,17 +33,17 @@ The species list is derived from data available at <https://avibase.bsc-eoc.org/
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  import birder
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  from birder.inference.classification import infer_image
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- (net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("swin_transformer_v2_s_intermediate-arabian-peninsula", inference=True)
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  # Get the image size the model was trained on
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- size = birder.get_size_from_signature(signature)
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  # Create an inference transform
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- transform = birder.classification_transform(size, rgb_stats)
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  image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format
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  (out, _) = infer_image(net, image, transform)
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- # out is a NumPy array with shape of (1, num_classes), representing class probabilities.
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  ```
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  ### Image Embeddings
@@ -52,17 +52,17 @@ image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format
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  import birder
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  from birder.inference.classification import infer_image
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- (net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("swin_transformer_v2_s_intermediate-arabian-peninsula", inference=True)
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  # Get the image size the model was trained on
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- size = birder.get_size_from_signature(signature)
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  # Create an inference transform
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- transform = birder.classification_transform(size, rgb_stats)
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  image = "path/to/image.jpeg" # or a PIL image
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  (out, embedding) = infer_image(net, image, transform, return_embedding=True)
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- # embedding is a NumPy array with shape of (1, embedding_size)
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  ```
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  ### Detection Feature Map
@@ -71,13 +71,13 @@ image = "path/to/image.jpeg" # or a PIL image
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  from PIL import Image
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  import birder
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- (net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("swin_transformer_v2_s_intermediate-arabian-peninsula", inference=True)
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  # Get the image size the model was trained on
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- size = birder.get_size_from_signature(signature)
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  # Create an inference transform
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- transform = birder.classification_transform(size, rgb_stats)
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  image = Image.open("path/to/image.jpeg")
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  features = net.detection_features(transform(image).unsqueeze(0))
 
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  import birder
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  from birder.inference.classification import infer_image
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+ (net, model_info) = birder.load_pretrained_model("swin_transformer_v2_s_intermediate-arabian-peninsula", inference=True)
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  # Get the image size the model was trained on
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+ size = birder.get_size_from_signature(model_info.signature)
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  # Create an inference transform
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+ transform = birder.classification_transform(size, model_info.rgb_stats)
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  image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format
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  (out, _) = infer_image(net, image, transform)
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+ # out is a NumPy array with shape of (1, 735), representing class probabilities.
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  ```
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  ### Image Embeddings
 
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  import birder
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  from birder.inference.classification import infer_image
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+ (net, model_info) = birder.load_pretrained_model("swin_transformer_v2_s_intermediate-arabian-peninsula", inference=True)
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  # Get the image size the model was trained on
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+ size = birder.get_size_from_signature(model_info.signature)
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  # Create an inference transform
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+ transform = birder.classification_transform(size, model_info.rgb_stats)
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  image = "path/to/image.jpeg" # or a PIL image
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  (out, embedding) = infer_image(net, image, transform, return_embedding=True)
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+ # embedding is a NumPy array with shape of (1, 768)
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  ```
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  ### Detection Feature Map
 
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  from PIL import Image
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  import birder
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+ (net, model_info) = birder.load_pretrained_model("swin_transformer_v2_s_intermediate-arabian-peninsula", inference=True)
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  # Get the image size the model was trained on
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+ size = birder.get_size_from_signature(model_info.signature)
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  # Create an inference transform
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+ transform = birder.classification_transform(size, model_info.rgb_stats)
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  image = Image.open("path/to/image.jpeg")
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  features = net.detection_features(transform(image).unsqueeze(0))