| | --- |
| | tags: |
| | - image-classification |
| | - birder |
| | - pytorch |
| | library_name: birder |
| | license: apache-2.0 |
| | --- |
| | |
| | # Model Card for nextvit_s_eu-common |
| |
|
| | A Next-ViT small 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 `nextvit_s_eu-common256px`. |
| |
|
| | ## Model Details |
| |
|
| | - **Model Type:** Image classification and detection backbone |
| | - **Model Stats:** |
| | - Params (M): 31.5 |
| | - Input image size: 384 x 384 |
| | - **Dataset:** eu-common (707 classes) |
| |
|
| | - **Papers:** |
| | - Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios: <https://arxiv.org/abs/2207.05501> |
| |
|
| | ## Model Usage |
| |
|
| | ### Image Classification |
| |
|
| | ```python |
| | import birder |
| | from birder.inference.classification import infer_image |
| | |
| | (net, model_info) = birder.load_pretrained_model("nextvit_s_eu-common", inference=True) |
| | # Note: A 256x256 variant is available as "nextvit_s_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("nextvit_s_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, 1024) |
| | ``` |
| |
|
| | ### Detection Feature Map |
| |
|
| | ```python |
| | from PIL import Image |
| | import birder |
| | |
| | (net, model_info) = birder.load_pretrained_model("nextvit_s_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, 256, 48, 48])), |
| | # ('stage3', torch.Size([1, 512, 24, 24])), |
| | # ('stage4', torch.Size([1, 1024, 12, 12]))] |
| | ``` |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @misc{li2022nextvitgenerationvisiontransformer, |
| | title={Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios}, |
| | author={Jiashi Li and Xin Xia and Wei Li and Huixia Li and Xing Wang and Xuefeng Xiao and Rui Wang and Min Zheng and Xin Pan}, |
| | year={2022}, |
| | eprint={2207.05501}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV}, |
| | url={https://arxiv.org/abs/2207.05501}, |
| | } |
| | ``` |
| |
|
