File size: 2,998 Bytes
7f01d5f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
---
tags:
- image-classification
- birder
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 (all the relevant bird species found in Israel inc. rarities).
The specie list is based on data from <https://www.israbirding.com/checklist/>.
## Model Details
- **Model Type:** Image classification / detection backbone
- **Model Stats:**
- Params (M): 28.0
- 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, class_to_idx, signature, rgb_stats) = 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(signature)
# Create an inference transform
transform = birder.classification_transform(size, rgb_stats)
image = "path/to/image.jpeg" # or a PIL image
(out, _) = infer_image(net, image, transform)
# out is a NumPy array with shape of (1, num_classes)
```
### Image Embeddings
```python
import birder
from birder.inference.classification import infer_image
(net, class_to_idx, signature, rgb_stats) = 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(signature)
# Create an inference transform
transform = birder.classification_transform(size, 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, embedding_size)
```
### Detection Feature Map
```python
from PIL import Image
import birder
(net, class_to_idx, signature, rgb_stats) = 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(signature)
# Create an inference transform
transform = birder.classification_transform(size, 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}
}
```
|