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README.md
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@@ -35,17 +35,17 @@ The species list is derived from the Collins bird guide [^1].
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import birder
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from birder.inference.classification import infer_image
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(net,
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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,
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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,
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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,
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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,
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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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# features is a dict (stage name -> torch.Tensor)
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print([(k, v.size()) for k, v in features.items()])
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# Output example:
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# [('stage1', torch.Size([1,
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# ('stage2', torch.Size([1,
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# ('stage3', torch.Size([1,
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# ('stage4', torch.Size([1,
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```
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## Citation
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```bibtex
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@misc{radosavovic2020designingnetworkdesignspaces,
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title={Designing Network Design Spaces},
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author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
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year={2020},
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eprint={2003.13678},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2003.13678},
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}
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```
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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("regnet_y_8g_intermediate-eu-common", 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, 707), 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("regnet_y_8g_intermediate-eu-common", 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, 2016)
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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("regnet_y_8g_intermediate-eu-common", 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))
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# features is a dict (stage name -> torch.Tensor)
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print([(k, v.size()) for k, v in features.items()])
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# Output example:
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# [('stage1', torch.Size([1, 224, 96, 96])),
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# ('stage2', torch.Size([1, 448, 48, 48])),
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# ('stage3', torch.Size([1, 896, 24, 24])),
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# ('stage4', torch.Size([1, 2016, 12, 12]))]
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```
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## Citation
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```bibtex
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@misc{radosavovic2020designingnetworkdesignspaces,
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title={Designing Network Design Spaces},
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author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
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year={2020},
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eprint={2003.13678},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2003.13678},
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}
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```
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