Some talks summarize all of your work, plus talk on those aspects which you did implicitly part of your work. Nice Talk to connect with Vision Work :)
Key Notes
- Workflow for Image Solutions
- Dataset preparation
- Data Annotation
- Training / Benchmark
- Pre-trained + Transfer Vs Custom Model
- Metrics for benchmarking
Data Collection
- Data set type
- Streaming or Image
- Data formats
- Single image / frames
- Video - Frame - Feed model
- Image Resolution / Frame rates sampling
- Reduce frame rate to support more streams
- Preprocessing work
- Crop noisy areas
- Select areas of interest
- Data Generation
- Data Augmentation
- Simple techniques including vision tricks - rotation, transformation, different angles
- GAN / Synthetic data generation techniques
Data Annotate
- Annotate / Review
- Validate with SME
- Bounding boxes / Segmentation / Labels
- Single / Multiple objects / Classes
- Occlusion, Light, settings
- Partially available surfaces
- Fine-grained annotation or not
- Data set representation against bias
- Coverage of possible classes
- Models for Day time vs Night time
Model Training
- Segmentation / Custom Detection
- Post-processing
- Transfer Learning
Model Optimization
- Prune / Quantize
- Inference Engines
- CPU / GPU / FPGA
Benchmark
- Testing on Deployable hardware
- Number of endpoints
- Load vs Response
- Re-annotate / Re-train
- Ensemble or Single Model
Deploy
- Edge vs Cloud
- Edge Server - Lite weight models
- Address based on the constraint, workloads for edge devices
- Hybrid approach both edge + cloud
- Model interface with application
- Storing Results in DB
- Real-time notification or just store
Model Monitoring
- Monitoring for data/accuracy of detections
- Pick low accuracy results / retrain them
- Capture when confidence is less than 50%
- Continuous re-learning
End to End platform for this
Keep Connecting the Dots!!!
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