Installation Guide
# Create and activate environment
conda create -n doraemon python=3.10 -y && conda activate doraemon
# Install PyTorch (CUDA or CPU version)
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
# or
conda install pytorch torchvision torchaudio cpuonly -c pytorch -y
# Install dependencies
pip install -r requirements.txt
# For CBIR functionality
conda install faiss-gpu=1.8.0 -c pytorch -y
# Optional: Install Arial font for faster inference
mkdir -p ~/.config/Doraemon && cp misc/Arial.ttf ~/.config/Doraemon- [Oct. 2024] Content-Based Image Retrieval(CBIR) support added with ConvNext backbone
- [Apr. 2024] [Face Recognition Task(FRT)] launched with various backbones and loss functions
- [Jun. 2023] Image Classification Task(ICT) released with advanced training strategies
- [May. 2023] Initial release of Doraemon
| Category | Methods |
|---|---|
| Optimization | SAM, Progressive Learning, OHEM, Focal Loss, Cosine Annealing |
| Regularization | Label Smoothing, Mixup, CutOut |
| Attention & Visualization | Attention Pool, GradCAM |
| Representation Learning | ArcFace, CircleLoss, MegFace, MV Softmax |
Doraemon now supports 1000+ models through integration with Timm:
- All models from
timm.list_models(pretrained=True) - Including CLIP, SigLIP, DeiT, BEiT, MAE, EVA, DINO and more
Model Performance Benchmarks can help you select the most suitable model by comparing:
- Inference speed
- Training efficiency
- Accuracy across different datasets
- Parameter count vs performance trade-offs
For detailed benchmark results, see @huggingface/pytorch-image-models#1933