File size: 3,090 Bytes
ab97821 |
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
- pytorch
library_name: birder
license: apache-2.0
---
# Model Card for efficientvim_m1_il-common
A EfficientViM image classification model. This model was trained on the `il-common` dataset, which contains common bird species found in Israel.
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): 6.1
- Input image size: 256 x 256
- **Dataset:** il-common (371 classes)
- **Papers:**
- EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality: <https://arxiv.org/abs/2411.15241>
## Model Usage
### Image Classification
```python
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-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, must be loaded in RGB format
(out, _) = infer_image(net, image, transform)
# out is a NumPy array with shape of (1, 371), representing class probabilities.
```
### Image Embeddings
```python
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-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, 320)
```
### Detection Feature Map
```python
from PIL import Image
import birder
(net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-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, 128, 16, 16])),
# ('stage2', torch.Size([1, 192, 8, 8])),
# ('stage3', torch.Size([1, 320, 4, 4]))]
```
## Citation
```bibtex
@misc{lee2025efficientvimefficientvisionmamba,
title={EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality},
author={Sanghyeok Lee and Joonmyung Choi and Hyunwoo J. Kim},
year={2025},
eprint={2411.15241},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.15241},
}
```
|