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---
base_model: facebook/detr-resnet-50
base_model_relation: finetune
license: mit
library_name: transformers
pipeline_tag: object-detection
tags:
- spheres
- photogrammetry
- calibration
widget:
- src: examples/chevaux.jpg
example_title: Chevaux
- src: examples/mammouths.jpg
example_title: Mammouths
- src: examples/synth.png
example_title: Synth
---
# Model card for REVA-QCAV
A DEtection TRansformer (DETR) model with a ResNet-50 backbone ([facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)) finetuned on a custom photogrammetry calibration sphere dataset.
## Model Usage
### Object Detection (using `transformers`)
```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from huggingface_hub import hf_hub_download
from PIL import Image
import torch
# download example image
img_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="examples/chevaux.jpg")
img = Image.open(img_path)
# transform image using image_processor
image_processor = AutoImageProcessor.from_pretrained("1aurent/REVA-QCAV")
data = image_processor(img, return_tensors="pt")
# get outputs from the model
model = AutoModelForObjectDetection.from_pretrained("1aurent/REVA-QCAV")
with torch.no_grad():
output = model(**data)
# use image_processor post processing
img_CHW = torch.tensor([img.height, img.width]).unsqueeze(0)
output_processed = image_processor.post_process_object_detection(output, threshold=0.9, target_sizes=img_CHW)
```
### Object Detection (using `onnxruntime`)
```python
from transformers.models.detr.modeling_detr import DetrObjectDetectionOutput
from transformers import AutoImageProcessor
from huggingface_hub import hf_hub_download
import onnxruntime as ort
from PIL import Image
import torch
# download onnx and start inference session
onnx_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="model.onnx")
session = ort.InferenceSession(onnx_path)
# download example image
img_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="examples/chevaux.jpg")
img = Image.open(img_path)
# transform image using image_processor
image_processor = AutoImageProcessor.from_pretrained("1aurent/REVA-QCAV")
data = image_processor(img, return_tensors="np").data
# get logits and bbox predictions using onnx session
logits, pred_boxes = session.run(
output_names=["logits", "pred_boxes"],
input_feed=data,
)
# wrap outputs inside DetrObjectDetectionOutput
output = DetrObjectDetectionOutput(
logits=torch.tensor(logits),
pred_boxes=torch.tensor(pred_boxes),
)
# use image_processor post processing
img_CHW = torch.tensor([img.height, img.width]).unsqueeze(0)
output_processed = image_processor.post_process_object_detection(output, threshold=0.9, target_sizes=img_CHW)
```
## Citation
```bibtex
@article{reva-qcav,
author = {Laurent Fainsin and Jean Mélou and Lilian Calvet and Antoine Laurent and Axel Carlier and Jean-Denis Durou},
title = {Neural sphere detection in images for lighting calibration},
journal = {QCAV},
year = {2023},
url = {https://hal.science/hal-04160733}
}
```
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