| --- |
| tags: |
| - image-classification |
| - birder |
| - pytorch |
| library_name: birder |
| license: apache-2.0 |
| --- |
| |
| # Model Card for convnext_v2_tiny_eu-common |
| |
| A ConvNeXt v2 Tiny image classification model. This model was trained on the `eu-common` dataset containing common European bird species. |
| |
| The species list is derived from the Collins bird guide [^1]. |
| |
| [^1]: Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins. |
| |
| Note: A 256 x 256 variant of this model is available as `convnext_v2_tiny_eu-common256px`. |
|
|
| ## Model Details |
|
|
| - **Model Type:** Image classification and detection backbone |
| - **Model Stats:** |
| - Params (M): 28.4 |
| - Input image size: 384 x 384 |
| - **Dataset:** eu-common (707 classes) |
|
|
| - **Papers:** |
| - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: <https://arxiv.org/abs/2301.00808> |
|
|
| ## Model Usage |
|
|
| ### Image Classification |
|
|
| ```python |
| import birder |
| from birder.inference.classification import infer_image |
| |
| (net, model_info) = birder.load_pretrained_model("convnext_v2_tiny_eu-common", inference=True) |
| # Note: A 256x256 variant is available as "convnext_v2_tiny_eu-common256px" |
| |
| # 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, 707), representing class probabilities. |
| ``` |
|
|
| ### Image Embeddings |
|
|
| ```python |
| import birder |
| from birder.inference.classification import infer_image |
| |
| (net, model_info) = birder.load_pretrained_model("convnext_v2_tiny_eu-common", 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 |
|
|
| ```python |
| from PIL import Image |
| import birder |
| |
| (net, model_info) = birder.load_pretrained_model("convnext_v2_tiny_eu-common", 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)) |
| # features is a dict (stage name -> torch.Tensor) |
| print([(k, v.size()) for k, v in features.items()]) |
| # Output example: |
| # [('stage1', torch.Size([1, 96, 96, 96])), |
| # ('stage2', torch.Size([1, 192, 48, 48])), |
| # ('stage3', torch.Size([1, 384, 24, 24])), |
| # ('stage4', torch.Size([1, 768, 12, 12]))] |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{woo2023convnextv2codesigningscaling, |
| title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, |
| author={Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In So Kweon and Saining Xie}, |
| year={2023}, |
| eprint={2301.00808}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2301.00808}, |
| } |
| ``` |
|
|
