Welcome to your journey to becoming a Computer Vision Master using PyTorch! This comprehensive roadmap outlines a detailed week-by-week plan, including specific learning objectives, hands-on projects, and essential concepts. We focus only on PyTorch throughout to give you deep, practical expertise.
🎯 Objectives:
- Understand image types (RGB, grayscale), tensors, and PyTorch structure
- Master PyTorch tensors, training loop, optimizers, and loss functions
- Build and visualize a basic CNN
📘 Learn This:
torch.tensor(),.shape,.view(),.unsqueeze()autograd,.backward(), gradient flownn.Module,forward(),nn.Linear,nn.Conv2d,nn.ReLU- Optimizers:
torch.optim.SGD,Adam, LR scheduling - Loss functions:
nn.CrossEntropyLoss,nn.MSELoss
🧪 Mini Project:
- Train 2-layer CNN on MNIST
- Visualize predictions and training curves
🧠 Master Concepts:
- Autograd, weights vs. activations
- Underfitting vs. overfitting
🎯 Objectives:
- Understand classic CNNs and pre-trained model use
📘 Learn This:
- Architecture: LeNet, AlexNet, VGG, ResNet, MobileNet
- Layers:
nn.Conv2d,MaxPool2d,BatchNorm2d,Dropout - Freezing/unfreezing layers, transfer learning strategies
- Using
torchvision.models
🧪 Mini Project:
- Fine-tune ResNet18 on TrashNet
- Compare feature extraction vs. full fine-tuning
🧠 Master Concepts:
- Residual blocks, depth vs. width, overfitting control
🎯 Objectives:
- Implement efficient data pipelines
📘 Learn This:
torchvision.transforms:Compose,Resize,ToTensor,Normalize,RandomCrop- Custom
Datasetclass:__len__,__getitem__ DataLoaderwithnum_workers,pin_memory- Imbalance handling:
WeightedRandomSampler
🧪 Mini Project:
- Load and augment TrashNet
- Visualize batches with Matplotlib
🧠 Master Concepts:
- Why test/val transforms differ
- Overfitting prevention via augmentations
🎯 Objectives:
- Annotate, train, and evaluate object detectors
📘 Learn This:
- YOLOv5/v8 architecture: backbone, neck, head
- Metrics: IoU, Precision, Recall, mAP
- Annotation tools (Roboflow), formats (YOLO, COCO)
- Training YOLO with custom configs
🧪 Mini Project:
- Annotate TrashNet
- Train YOLOv5s and evaluate on webcam
🧠 Master Concepts:
- Anchor boxes, NMS, confidence thresholds
- Bounding box loss types (CIoU, GIoU)
🎯 Objectives:
- Segment objects at pixel level
📘 Learn This:
- UNet architecture (skip connections, upsampling)
- Mask R-CNN, DeepLabV3+ overview
- Loss functions: Dice, BCEWithLogits, Focal
- Using
SegmentationModels-PyTorch
🧪 Mini Project:
- Train UNet on TrashNet masks
- Overlay masks using OpenCV
🧠 Master Concepts:
- Binary vs. multiclass segmentation
- Pixel-wise accuracy, Dice coefficient
🎯 Objectives:
- Interpret models using saliency and gradient methods
📘 Learn This:
- Grad-CAM, Integrated Gradients (Captum)
- Visualize filters, activations, saliency maps
🧪 Mini Project:
- Apply Grad-CAM on misclassified samples
- Visualize layer-wise features
🧠 Master Concepts:
- Explainability for trust & debugging
- ReLU and gradient flow
🎯 Objectives:
- Optimize and export models to run outside Python
📘 Learn This:
- TorchScript:
torch.jit.scriptvs.trace - Quantization: static, dynamic, aware
- ONNX export, OpenCV DNN inference
🧪 Mini Project:
- Export YOLO model to ONNX
- Run inference with OpenCV
🧠 Master Concepts:
- Deployment trade-offs: speed vs. accuracy
- Model compression techniques
🎯 Objectives:
- Work with CLIP, DINO, and SAM
📘 Learn This:
- CLIP embeddings (image + text)
- Segment Anything Model architecture
- SimCLR, DINO concepts
🧪 Mini Project:
- Use CLIP for zero-shot classification
- Combine CLIP + SAM to segment and describe objects
🧠 Master Concepts:
- Vision-language alignment
- Promptable segmentation
🎯 Objectives:
- Master transformer architecture in the context of computer vision
📘 Learn This:
- Attention mechanism, Multi-Head Self-Attention, Positional Encoding
- ViT (Vision Transformer): Patch embedding, tokenization
- MAE (Masked Autoencoders), DeiT, Swin Transformer
- Hugging Face
transformersandtimmintegration
🧪 Mini Project:
- Implement or finetune ViT for TrashNet classification
- Visualize attention maps from ViT model
🧠 Master Concepts:
- CNN vs Transformer: inductive bias, locality vs globality
- Vision Transformers for classification, detection, and segmentation
🎯 Objective:
- Integrate your learnings into a full pipeline project
Project Ideas:
- Waste Sorting Assistant (YOLO + UNet + Streamlit)
- Drone Surveillance (YOLO + ViT)
- Sign Language Recognition (CNN + RNN)
Deliverables:
- GitHub repo
- Streamlit/Gradio demo
- ONNX/TorchScript deployment
- PyTorch, Torchvision, Albumentations, OpenCV
- Matplotlib, Seaborn, Captum
- Hugging Face Transformers, Timm
- Gradio, Streamlit, Roboflow
- ONNX, TorchScript
Learning CV is like training a deep net—hard at first, but incredible once it converges. Keep iterating, build mini-projects, and teach others what you learn. By the end of these 13 weeks, you’ll be capable of building production-grade vision applications using PyTorch and GenAI models.
Ready to flex those neurons? Let’s gooo 🧠🔥