Model Card for rdnet_s_arabian-peninsula
An RDNet image classification model. This model was trained on the arabian-peninsula
dataset (all the relevant bird species found in the Arabian peninsula inc. rarities).
The species list is derived from data available at https://avibase.bsc-eoc.org/checklist.jsp?region=ARA.
Note: A 256 x 256 variant of this model is available as rdnet_s_arabian-peninsula256px
.
Model Details
Model Type: Image classification and detection backbone
Model Stats:
- Params (M): 50.1
- Input image size: 384 x 384
Dataset: arabian-peninsula (735 classes)
Papers:
- DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs: https://arxiv.org/abs/2403.19588
Model Usage
Image Classification
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("rdnet_s_arabian-peninsula", inference=True)
# Note: A 256x256 variant is available as "rdnet_s_arabian-peninsula256px"
# 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, 735), representing class probabilities.
Image Embeddings
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("rdnet_s_arabian-peninsula", 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, 1264)
Detection Feature Map
from PIL import Image
import birder
(net, model_info) = birder.load_pretrained_model("rdnet_s_arabian-peninsula", 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, 264, 96, 96])),
# ('stage2', torch.Size([1, 512, 48, 48])),
# ('stage3', torch.Size([1, 760, 24, 24])),
# ('stage4', torch.Size([1, 1264, 12, 12]))]
Citation
@misc{kim2024densenetsreloadedparadigmshift,
title={DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs},
author={Donghyun Kim and Byeongho Heo and Dongyoon Han},
year={2024},
eprint={2403.19588},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2403.19588},
}
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