sky engine Flashcards
learn the jd
List the things I will do
● Develop Deep Learning solutions in various Computer Vision tasks
● Develop the methodology and assessment criteria to measure solution performance
● Track and implement recent advancements in Deep Learning and Computer Vision areas
● Maintain internal ML-products
What do I have - Comuter vision
Experience with deep learning algorithms applied to various areas of Computer Vision
● Familiarity with popular architectures such as Vision Transformers, DeepLabv3, SegFormer, etc
● Good understanding of transfer learning and popular augmentation techniques
● Proven experience in tackling common computer vision tasks including image classification,
detection, segmentation, face recognition, pose estimation, etc
● Working knowledge of CV-related training procedures, validation techniques and datasets
What do I have - general ML
The ability to apply machine learning to real-life problems
● Good knowledge of machine learning algorithms, model training, hyper parameter tuning
● Working knowledge of deep learning models, loss functions, and performance measures
Strong Python skills, hands-on proficiency in using machine learning tools: Scikit-Learn, NumPy,
Pandas and PyTorch/TensorFlow/Keras
● The ability to share your knowledge and willingness to learn continuously
● English B2+ level at minimum
What are the extra skills that would be a plus?
NLP
● OpenCV
● Generative AI
● data visualization
● building data processing pipelines, MLOps
Q1: What industries does Sky Engine focus on initially?
A1: Sky Engine focuses on healthcare, sports, manufacturing, and agriculture as its initial industries.
What computer vision architectures should a candidate be familiar with for this role?
Candidates should be familiar with architectures such as Vision Transformers, DeepLabv3, and SegFormer.
How would you optimize a YOLO model for deployment on edge devices?
To optimize YOLO for edge deployment, I’d first reduce the model size using pruning to remove less critical weights. Quantization can help by converting weights from 32-bit to 8-bit, reducing memory and computation. I’d also explore lightweight versions, like YOLOv5n or YOLOv4-tiny. Data-specific fine-tuning ensures efficiency, and I’d optimize inference with TensorRT or ONNX. Finally, reducing input resolution balances accuracy with speed for real-time edge performance.
What are common challenges in facial recognition systems, and how can they be addressed?
Challenges include lighting variations, occlusions (like masks), pose variations, and demographic bias. To address these, augmenting data with variations helps improve generalization. Techniques like histogram equalization can standardize lighting. Multi-angle datasets mitigate pose issues. To counter bias, ensuring diverse training datasets and using fairness-aware algorithms are crucial. Robust feature extraction with deep models like ArcFace or FaceNet also enhances system reliability.
Describe the working of the SegFormer architecture.
SegFormer combines transformer-based encoders with lightweight MLP decoders for semantic segmentation. The encoder captures global features using self-attention, ensuring high spatial understanding. Unlike traditional CNNs, it doesn’t rely on convolutions, allowing better adaptability to complex patterns. The MLP decoder processes multi-scale features to output segmentation maps efficiently, making SegFormer lightweight and effective for diverse tasks.
Q: Explain the difference between SSD and Faster R-CNN object detection models.
A: SSD (Single Shot Multibox Detector) performs object detection in a single step, offering speed but slightly lower accuracy, ideal for real-time applications. Faster R-CNN uses a two-step process: generating region proposals and refining them, achieving higher accuracy at the cost of slower inference. SSD is simpler and lighter, while Faster R-CNN is more suited for precision-critical tasks.
Q: What are adversarial attacks, and how can you make your computer vision model more robust to them?
digital and physics
digital = image perturbations
physical = add objects for misclassification for example.
More physical.
Data augmentations - objects insert
Q: How would you preprocess data for a face recognition task?
Data augmentation, including flips, cropping, resizing, rotations, or lighting adjustments, enhances robustness. Lastly, embeddings are extracted using pre-trained models like FaceNet.
Q: Explain the role of batch normalization in deep learning.
A: Batch normalization normalizes activations within a layer to stabilize training and accelerate convergence. It reduces internal covariate shift, allowing models to use higher learning rates. By adding learnable scale and shift parameters, it preserves model capacity while reducing overfitting. This improves both training stability and final model performance.
Q: How does transfer learning differ when applied to transformers versus CNNs?
A: In CNNs, transfer learning often involves fine-tuning or freezing convolutional layers pre-trained on similar datasets. Transformers, like Vision Transformers, require re-tuning on new data due to their reliance on global attention. Transformers also demand larger datasets and benefit more from domain-specific pre-training for effective transfer.
Q: Explain how a Siamese network works for image matching.
A: A Siamese network consists of two identical subnetworks that learn feature embeddings for input pairs. It uses a contrastive loss or triplet loss to minimize the distance between embeddings of similar images and maximize it for dissimilar ones. This structure is ideal for tasks like image matching, face verification, and one-shot learning, as it learns relationships rather than explicit categories.
