Hello,
We are excited about the move to PyTorch and the architectural improvements in v4.0. To support better accuracy in the South Asian region, we are interested in providing a comprehensive dataset of Indian license plates.
To ensure the data is immediately useful for your new PyTorch-based pipeline, could you please clarify:
Annotation Format: Since you are moving to PyTorch, do you prefer YOLO (txt), COCO (json), or a specific XML format for bounding boxes and character labels?
Metadata Requirements: Should we include labels for vehicle make/model (VMMR) and color, or strictly focus on the plate and OCR characters?
Submission Method: What is your preferred method for transferring large datasets (e.g., Secure FTP, Google Drive link, or a specific repository)?
We look forward to contributing to the improvement of v4.0.
Regards,
Vishnu P
PETAERA TECHNOLOGIES LLP
Hi,
As discussed earlier, we’ve shared the dataset privately via email from vis...@petaera.com to the support team, as suggested.
Just wanted to confirm whether it was received successfully, and please let us know if anything else is required from our side.
Regards,
Vishnu P
PETAERA TECHNOLOGIES LLP
Hi,
Thanks for confirming that the mail with the dataset was received.
Whenever convenient, I’d appreciate any feedback or response on the observations and issues related to ALPR version 3 that were mentioned in the same email. Please let me know if any further clarification or inputs are required from our side.
Looking forward to your response.
Regards,
Vishnu P
PETAERA TECHNOLOGIES LLP