torchvision
Overview
TorchVision provides models, datasets, and transforms for computer vision. It has recently transitioned to "v2" transforms, which support more complex data types like bounding boxes and masks alongside images, using a unified API.
When to Use
Use TorchVision for standard CV tasks like classification, detection, or segmentation. Use the v2 transforms for performance-critical pipelines or when applying augmentations like MixUp/CutMix that require batch-level processing.
Decision Tree
- Are you starting a new project?
- YES: Use
torchvision.transforms.v2.
- YES: Use
- Do you need a pretrained model?
- YES: Use the
weightsparameter (e.g.,ResNet50_Weights.DEFAULT).
- YES: Use the
- Do you have bounding boxes that need to move with the image?
- YES: Use
TVTensorsfor automatic coordinate transformation.
- YES: Use
Workflows
More from cuba6112/skillfactory
ollama-rag
Build RAG systems with Ollama local + cloud models. Latest cloud models include DeepSeek-V3.2 (GPT-5 level), Qwen3-Coder-480B (1M context), MiniMax-M2. Use for document Q&A, knowledge bases, and agentic RAG. Covers LangChain, LlamaIndex, ChromaDB, and embedding models.
17unsloth-sft
Supervised fine-tuning using SFTTrainer, instruction formatting, and multi-turn dataset preparation with triggers like sft, instruction tuning, chat templates, sharegpt, alpaca, conversation_extension, and SFTTrainer.
6torchaudio
Audio signal processing library for PyTorch. Covers feature extraction (spectrograms, mel-scale), waveform manipulation, and GPU-accelerated data augmentation techniques. (torchaudio, melscale, spectrogram, pitchshift, specaugment, waveform, resample)
5pytorch-onnx
Exporting PyTorch models to ONNX format for cross-platform deployment. Includes handling dynamic axes, graph optimization in ONNX Runtime, and INT8 model quantization. (onnx, onnxruntime, torch.onnx.export, dynamic_axes, constant-folding, edge-deployment)
5unsloth-lora
Configuring and optimizing 16-bit Low-Rank Adaptation (LoRA) and Rank-Stabilized LoRA (rsLoRA) for efficient LLM fine-tuning using triggers like lora, qlora, rslora, rank selection, lora_alpha, lora_dropout, and target_modules.
4pytorch-quantization
Techniques for model size reduction and inference acceleration using INT8 quantization, including Post-Training Quantization (PTQ) and Quantization Aware Training (QAT). (quantization, int8, qat, fbgemm, qnnpack, ptq, dequantize)
3