nvidia
/

nemotron-page-elements-v3

@@ -68,7 +68,7 @@ The **NeMo Retriever Page Elements v3** model  is designed for automating extrac
 **Architecture Type**: YOLOX <br>
 **Network Architecture**: DarkNet53 Backbone \+ FPN Decoupled head (one 1x1 convolution \+ 2 parallel 3x3 convolutions (one for the classification and one for the bounding box prediction). YOLOX is a single-stage object detector that improves on Yolo-v3. <br>
 **This model was developed based on the Yolo architecture** <br>
-**Number of model parameters**: $5.4*10^7$ <br>
 ### Input
@@ -146,10 +146,10 @@ with torch.inference_mode():
 print(preds)
 # Post-processing
-labels, bboxes, scores = postprocess_preds_page_element(preds, model.thresholds_per_class, model.labels)
 # Plot
-boxes_plot, confs = reformat_for_plotting(labels, bboxes, scores, img.shape, model.num_classes)
 plt.figure(figsize=(15, 10))
 plot_sample(img, boxes_plot, confs, labels=model.labels)
@@ -164,12 +164,11 @@ If you wish to do additional training, [refer to the original repo](https://gith
 Additional post-processing might be required to use the model as part of a data extraction pipeline.
 We provide examples in the notebook `Demo.ipynb`.
 ### Software Integration
-<!---
 **Runtime Engine(s):**
 - **NeMo Retriever Page Elements v3** NIM
---->
 **Supported Hardware Microarchitecture Compatibility [List in Alphabetic Order]:**
 - NVIDIA Ampere
@@ -181,6 +180,7 @@ We provide examples in the notebook `Demo.ipynb`.
 The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
 This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
 ## Model Version(s):

 **Architecture Type**: YOLOX <br>
 **Network Architecture**: DarkNet53 Backbone \+ FPN Decoupled head (one 1x1 convolution \+ 2 parallel 3x3 convolutions (one for the classification and one for the bounding box prediction). YOLOX is a single-stage object detector that improves on Yolo-v3. <br>
 **This model was developed based on the Yolo architecture** <br>
+**Number of model parameters**: 5.4e7 <br>
 ### Input
 print(preds)
 # Post-processing
+boxes, labels, scores = postprocess_preds_page_element(preds, model.thresholds_per_class, model.labels)
 # Plot
+boxes_plot, confs = reformat_for_plotting(boxes, labels, scores, img.shape, model.num_classes)
 plt.figure(figsize=(15, 10))
 plot_sample(img, boxes_plot, confs, labels=model.labels)
 Additional post-processing might be required to use the model as part of a data extraction pipeline.
 We provide examples in the notebook `Demo.ipynb`.
+<!---
 ### Software Integration
 **Runtime Engine(s):**
 - **NeMo Retriever Page Elements v3** NIM
 **Supported Hardware Microarchitecture Compatibility [List in Alphabetic Order]:**
 - NVIDIA Ampere
 The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
 This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
+--->
 ## Model Version(s):