Architect Intelligent Vision Systems. The Best Deep Learning & Vision Course in Ahmedabad.
Product teams ship cameras, not spreadsheets — they need engineers who train CNNs, tune YOLO detectors, and deploy ONNX models under latency budgets. We train you on PyTorch, torchvision, OpenCV, and production vision APIs used in manufacturing QA, retail analytics, automotive ADAS, and healthcare imaging across India's GCC and product hubs.
Enterprise Enrollment Architecture
Duration: 60 Hours
₹45,000₹32,000
Note: Any type of tax would be extra. Global certification cost is excluded.
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Bascom Bridge Alumni Engineering Global Tech
Exclusive Program Benefits
- After the course, Bascom Bridge will share 10–12 sample CVs to help build your resume.
- Students receive a license for Bascom Bridge’s placement mobile app*.
- Lifetime access* to the enrolled course for students.
- If a student does not clear interviews, Bascom Bridge will provide retraining* until employment is secured.
- Global certification training is included at no extra cost.
- Discount on global certification fees* available.
- GPU-backed labs for CNN training, transfer learning, and real-time inference pipelines.
- Portfolio capstone with YOLO detection API and ONNX export — interview-ready for CV engineer roles*.
Applicable taxes will be added to each instalment.
Trusted by Government of India & Leading PSUs
About Deep Learning & Vision
Enterprise Tools Included
- PyTorch & torchvision — CNNs, ResNet, transfer learning
- OpenCV — preprocessing, augmentation, classical CV pipelines
- Ultralytics YOLO — object detection & instance segmentation
- ONNX Runtime & TorchServe for model export and serving
- Weights & Biases / TensorBoard for experiment tracking
Cognitive Prerequisites
- Applied Data Science, Enterprise ML, or equivalent Python + ML foundations
- Comfort with NumPy, train/val splits, and classification metrics
- Linear algebra basics (tensors, matrix multiply) — refreshed in Module 1
- Laptop with 16 GB RAM; GPU cloud credits provided for heavy training labs
Salary Progression (₹)
- Entry-Level (0-3 yrs)₹5.0L - ₹12.0L
- Mid-Level (4-7 yrs)₹12.0L - ₹22.0L
- Senior Level (8-12+ yrs)₹22.0L - ₹35.0L+
The Enterprise Capstone Architecture
Industrial Defect Detection & PPE Compliance Platform. You will label a custom image dataset, train a YOLOv8 detector for surface defects and safety-gear violations, fine-tune a ResNet classifier for severity grading, export ONNX models for edge inference, and expose a FastAPI vision service in Docker with mAP benchmarks and failure-case dashboards — the same deliverable pattern manufacturing and logistics CV teams present in production reviews.
Deep-Dive Syllabus Grid
Module 1: Deep Learning Foundations for Vision
Tensors in PyTorch, autograd, GPU training loops, and the vision problem taxonomy — classification, detection, segmentation. How CNNs differ from tabular ML and what hiring panels at automotive, retail, and med-tech GCCs expect in a portfolio.
Module 2: Image Preprocessing & OpenCV Pipelines
Reading BGR/RGB frames, resizing, normalization, histogram equalization, morphological ops, and edge detection. Building reproducible preprocessing DAGs that match production camera feeds — not one-off notebook transforms.
Module 3: Convolutional Neural Networks from Scratch
Conv layers, pooling, receptive fields, and parameter efficiency. Implementing a baseline CNN in PyTorch, tracking loss curves, and diagnosing overfitting with augmentation and dropout — the core skill behind defect detection and document OCR.
Module 4: Transfer Learning with torchvision
Loading ImageNet-pretrained ResNet, EfficientNet, and MobileNet backbones; freezing versus fine-tuning; learning-rate schedules. Adapting models to Indian enterprise datasets — warehouse SKU recognition, PPE compliance, quality inspection.
Module 5: Data Augmentation & Class Imbalance
Albumentations-style transforms, mixup/cutmix overview, weighted sampling, and focal loss intuition. Handling long-tail classes in manufacturing and security footage where rare events matter most.
Module 6: Object Detection — YOLO Architecture
Bounding boxes, IoU, mAP metrics, anchor-free versus anchor-based detectors. Training YOLOv8 on custom datasets, labeling workflows, and exporting models for edge deployment — the stack used in smart city and logistics startups.
Module 7: Semantic & Instance Segmentation Basics
U-Net and Mask R-CNN concepts, pixel-wise loss, and when segmentation beats detection. Use cases: medical imaging overlays, agricultural crop boundaries, and lane marking for ADAS proof-of-concepts.
Module 8: Video Analytics & Multi-Frame Inference
Frame sampling, temporal smoothing, tracking-by-detection with OpenCV trackers, and FPS/latency trade-offs. Building people-counting and intrusion-alert prototypes suitable for Gujarat industrial parks and retail chains.
Module 9: Model Optimization — ONNX, Quantization & Edge
Exporting PyTorch to ONNX, INT8 quantization awareness, TensorRT overview, and batching strategies. Reducing inference cost for cameras on factory floors where cloud round-trips are unacceptable.
Module 10: Vision APIs with FastAPI & Docker
REST endpoints for image upload, batch scoring, health checks, and Pydantic validation. Containerizing vision services with GPU optional profiles — mirroring how product teams integrate CV into ERP and mobile apps.
Module 11: Responsible Vision AI & Bias Audits
Dataset bias, demographic fairness checks, consent for facial analytics, and DPDP-aligned data handling in India. Documenting model limitations for compliance reviews in banking KYC and public-sector surveillance tenders.
Module 12: Capstone Review & CV Engineer Interviews
System-design drills: camera ingest, model versioning, rollback, and monitoring false positives. Presenting mAP benchmarks, failure-case galleries, and latency SLAs — the interview bar for computer vision roles in Ahmedabad and Bengaluru product offices.
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