|
--- |
|
tags: |
|
- image-classification |
|
- birder |
|
- pytorch |
|
library_name: birder |
|
license: apache-2.0 |
|
--- |
|
|
|
# Model Card for davit_tiny_il-all |
|
|
|
A Dual Attention Vision Transformer (DaViT) image classification model. This model was trained on the `il-all` dataset, encompassing all relevant bird species found in Israel, including rarities. |
|
|
|
The species list is derived from data available at <https://www.israbirding.com/checklist/>. |
|
|
|
## Model Details |
|
|
|
- **Model Type:** Image classification and detection backbone |
|
- **Model Stats:** |
|
- Params (M): 28.0 |
|
- Input image size: 384 x 384 |
|
- **Dataset:** il-all (550 classes) |
|
|
|
- **Papers:** |
|
- DaViT: Dual Attention Vision Transformers: <https://arxiv.org/abs/2204.03645> |
|
|
|
## Model Usage |
|
|
|
### Image Classification |
|
|
|
```python |
|
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 |
|
|
|
```python |
|
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 |
|
|
|
```python |
|
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)) |
|
# 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{ding2022davitdualattentionvision, |
|
title={DaViT: Dual Attention Vision Transformers}, |
|
author={Mingyu Ding and Bin Xiao and Noel Codella and Ping Luo and Jingdong Wang and Lu Yuan}, |
|
year={2022}, |
|
eprint={2204.03645}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV}, |
|
url={https://arxiv.org/abs/2204.03645} |
|
} |
|
``` |
|
|
