Update README.md

README.md

@@ -13,4 +13,85 @@ widget:
   example_title: Mammouths
 - src: examples/synth.png
   example_title: Synth
----
+---
+
+# Model card for REVA-QCAV
+
+## 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}
+}
+```