Model Deployment
To successfully deploy your model, we require the following components:
- Model Overview:
- Please provide a concise description of the model, outlining the specific problem it addresses and its approach to solving that problem.
- Base Model or Deep Learning Architecture
- If your model is built upon an existing architecture or framework, such as Yolo family, Detectron2, ResNet, etc., please specify the architecture.
- If the model was designed from scratch, include the architecture details in PyTorch, TensorFlow, or ONNX format.
- Analytics/Post-processor code
- please provide the code If there is any analytics or post-processor algorithms are needed on top of the main model.
- Desired Output Format
- Specify the required columns or data in the output CSV. This will help ensure that the results from your model align with our needs.
- Annotation Preparation
- Clarify how you plan to prepare annotations for detections or AI-Clip results. Providing details about the annotation process will help streamline integration.
- Sample Output
- Including a sample output will offer clarity on the expected format and assist in verifying the results.
- Dependencies
- State the versions of PyTorch/TensorFlow and any other third-party libraries that your model depends on.
- Additionally, provide information about the CUDA version required for GPU acceleration.
- Deployment Script
- Preferably, supply a simple script that allows us to execute your model on our end. This will serve as a validation step, confirming that the model functions as intended in the deployment environment.
By providing these details, we can ensure a smooth deployment process and verify that your model aligns with our requirements.