birder-project
/

davit_tiny_il-all

Image Classification

Birder

PyTorch

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hassonofer commited on 10 days ago

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c515185

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1 Parent(s): 46507d9

Update README.md

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README.md +11 -11

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@@ -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("davit_tiny_il-all", inference=True)
 # Get the image size the model was trained on
-size = birder.get_size_from_signature(signature)
 # Create an inference transform
-transform = birder.classification_transform(size, 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.
 ```
 ### 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("davit_tiny_il-all", inference=True)
 # Get the image size the model was trained on
-size = birder.get_size_from_signature(signature)
 # Create an inference transform
-transform = birder.classification_transform(size, 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)
 ```
 ### 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("davit_tiny_il-all", inference=True)
 # Get the image size the model was trained on
-size = birder.get_size_from_signature(signature)
 # Create an inference transform
-transform = birder.classification_transform(size, rgb_stats)
 image = Image.open("path/to/image.jpeg")
 features = net.detection_features(transform(image).unsqueeze(0))

 import birder
 from birder.inference.classification import infer_image
+(net, model_info) = birder.load_pretrained_model("davit_tiny_il-all", inference=True)
 # Get the image size the model was trained on
+size = birder.get_size_from_signature(model_info.signature)
 # Create an inference transform
+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, 550), representing class probabilities.
 ```
 ### Image Embeddings
 import birder
 from birder.inference.classification import infer_image
+(net, model_info) = birder.load_pretrained_model("davit_tiny_il-all", inference=True)
 # Get the image size the model was trained on
+size = birder.get_size_from_signature(model_info.signature)
 # Create an inference transform
+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, 768)
 ```
 ### Detection Feature Map
 from PIL import Image
 import birder
+(net, model_info) = birder.load_pretrained_model("davit_tiny_il-all", inference=True)
 # Get the image size the model was trained on
+size = birder.get_size_from_signature(model_info.signature)
 # Create an inference transform
+transform = birder.classification_transform(size, model_info.rgb_stats)
 image = Image.open("path/to/image.jpeg")
 features = net.detection_features(transform(image).unsqueeze(0))