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eu-common dataset containing common European bird species.1import birder
2from birder.inference.classification import infer_image
3
4(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-common", inference=True)
5
6# Get the image size the model was trained on
7size = birder.get_size_from_signature(model_info.signature)
8
9# Create an inference transform
10transform = birder.classification_transform(size, model_info.rgb_stats)
11
12image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format
13(out, _) = infer_image(net, image, transform)
14# out is a NumPy array with shape of (1, 707), representing class probabilities.1import birder
2from birder.inference.classification import infer_image
3
4(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-common", inference=True)
5
6# Get the image size the model was trained on
7size = birder.get_size_from_signature(model_info.signature)
8
9# Create an inference transform
10transform = birder.classification_transform(size, model_info.rgb_stats)
11
12image = "path/to/image.jpeg" # or a PIL image
13(out, embedding) = infer_image(net, image, transform, return_embedding=True)
14# embedding is a NumPy array with shape of (1, 384)1from PIL import Image
2import birder
3
4(net, model_info) = birder.load_pretrained_model("vit_parallel_s16_18x2_ls_avg_data2vec-intermediate-eu-common", inference=True)
5
6# Get the image size the model was trained on
7size = birder.get_size_from_signature(model_info.signature)
8
9# Create an inference transform
10transform = birder.classification_transform(size, model_info.rgb_stats)
11
12image = Image.open("path/to/image.jpeg")
13features = net.detection_features(transform(image).unsqueeze(0))
14# features is a dict (stage name -> torch.Tensor)
15print([(k, v.size()) for k, v in features.items()])
16# Output example:
17# [('neck', torch.Size([1, 384, 24, 24]))]1@misc{touvron2022thingsknowvisiontransformers,
2 title={Three things everyone should know about Vision Transformers},
3 author={Hugo Touvron and Matthieu Cord and Alaaeldin El-Nouby and Jakob Verbeek and Hervé Jégou},
4 year={2022},
5 eprint={2203.09795},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2203.09795},
9}
10
11@misc{https://doi.org/10.48550/arxiv.2202.03555,
12 title={data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
13 author={Alexei Baevski and Wei-Ning Hsu and Qiantong Xu and Arun Babu and Jiatao Gu and Michael Auli},
14 year={2022},
15 eprint={2202.03555},
16 archivePrefix={arXiv},
17 primaryClass={cs.LG},
18 url={https://arxiv.org/abs/2202.03555},
19}