deeplite-torch-zoo 2.0.5

Description:

deeplitetorchzoo 2.0.5

🚀 Deeplite Torch Zoo 🚀 is a collection of state-of-the-art efficient
computer vision models for embedded applications in PyTorch.


For information on YOLOBench, click here.
The main features of this library are:

High-level API to create models, dataloaders, and evaluation functions
Single interface for SOTA classification models:

timm models,
pytorchcv models,
other SOTA efficient models (EdgeViT, FasterNet, GhostNetV2, MobileOne)


Single interface for SOTA YOLO detectors (compatible with Ultralytics training):

YOLOv3, v4, v5, v6-3.0, v7, v8
YOLO with timm backbones
other experimental configs



📋 Table of content

Quick start
Installation
Training scripts
Contributing
Credit

⏳ Quick start
Create a classification model
from deeplite_torch_zoo import get_model, list_models

model = get_model(
model_name='edgevit_xs', # model names for imagenet available via `list_models('imagenet')`
dataset_name='imagenet', # dataset name, since resnet18 is different for e.g. imagenet and cifar100
pretrained=False, # if True, will try to load a pre-trained checkpoint
)

# creating a model with 42 classes for transfer learning:

model = get_model(
model_name='fasternet_t0', # model names for imagenet available via `list_models('imagenet')`
num_classes=42, # number of classes for transfer learning
dataset_name='imagenet', # take weights from checkpoint pre-trained on this dataset
pretrained=False, # if True, will try to load all weights with matching tensor shapes
)

Create an object detection model
from deeplite_torch_zoo import get_model

model = get_model(
model_name='yolo4n', # creates a YOLOv4n model on COCO
dataset_name='coco', # (`n` corresponds to width factor 0.25, depth factor 0.33)
pretrained=False, # if True, will try to load a pre-trained checkpoint
)

# one could create a YOLO model with timm backbone,
# PAN neck and YOLOv8 decoupled anchor-free head like this:

model = get_model(
model_name='yolo_timm_fbnetv3_d', # creates a YOLO with FBNetV3-d backbone from timm
dataset_name='coco', #
pretrained=False, # if True, will try to load a pre-trained checkpoint
custom_head='yolo8', # will replace default detection head
# with YOLOv8 detection head
)

Create PyTorch dataloaders
from deeplite_torch_zoo import get_dataloaders

dataloaders = get_dataloaders(
data_root='./', # folder with data, will be used for download
dataset_name='imagewoof', # datasets to if applicable,
num_workers=8, # number of dataloader workers
batch_size=64, # dataloader batch size (train and test)
)

# dataloaders['train'] -> train dataloader
# dataloaders['test'] -> test dataloader
#
# see below for the list of supported datasets

The list of supported datasets is available for classification and object detection.
Creating an evaluation function
from deeplite_torch_zoo import get_eval_function

eval_function = get_eval_function(
model_name='yolo8s',
dataset_name='voc',
)

# required arg signature is fixed for all eval functions
metrics = eval_function(model, test_dataloader)

(Experimental) Training with patched Ultralytics trainer
from deeplite_torch_zoo.trainer import Detector

model = Detector(model_name='yolo7n') # will create a wrapper around YOLOv7n model
# (YOLOv7n model with YOLOv8 detection head)
model.train(data='VOC.yaml', epochs=100) # same arguments as Ultralytics trainer

# alternatively:

torch_model = get_model(
model_name='yolo7n',
dataset_name='coco',
pretrained=False,
custom_head='yolo8',
)
model = Detector(torch_model=torch_model) # either `model_name` or `torch_model`
model.train(data='VOC.yaml', epochs=100) # should be provided

🛠 Installation
PyPI version:
$ pip install deeplite-torch-zoo

Latest version from source:
$ pip install git+https://github.com/Deeplite/deeplite-torch-zoo.git

💪 Training scripts
We provide several training scripts as an example of how deeplite-torch-zoo can be integrated into existing training pipelines:


modified timm ImageNet script

support for Knowledge Distillation
training recipes provides (A1, A2, A3, USI, etc.)



modfied Ultralytics classification fine-tuning script


modfied Ultralytics YOLOv5 object detector training script


🤝 Contributing
We always welcome community contributions to expand the scope of deeplite-torch-zoo and also to have additional new models and datasets. Please refer to the documentation for the detailed steps on how to add a model and dataset. In general, we follow the fork-and-pull Git workflow.

Fork the repo on GitHub
Clone the project to your own machine
Commit changes to your own branch
Push your work back up to your fork
Submit a Pull request so that we can review your changes

NOTE: Be sure to merge the latest from "upstream" before making a pull request!
🙏 Credit

Repositories used to build Deeplite Torch Zoo
Object Detection

YOLOv3 implementation: ultralytics/yolov3
YOLOv5 implementation: ultralytics/yolov5
flexible-yolov5 implementation: Bobo-y/flexible-yolov5
YOLOv8 implementation: ultralytics/ultralytics
YOLOv7 implementation: WongKinYiu/yolov7
YOLOX implementation: iscyy/yoloair
westerndigitalcorporation/YOLOv3-in-PyTorch

Segmentation

The implementation of deeplab: pytorch-deeplab-xception
The implementation of unet_scse: nyoki-mtl/pytorch-segmentation
The implementation of fcn: wkentaro/pytorch-fcn
The implementation of Unet: milesial/Pytorch-UNet

Classification

The implementation of models on CIFAR100 dataset: kuangliu/pytorch-cifar
The implementation of Mobilenetv1 model on VWW dataset: qfgaohao/pytorch-ssd
The implementation of Mobilenetv3 model on VWW dataset: d-li14/mobilenetv3.pytorch

DNN building block implementations

d-li14/mobilenetv2.pytorch
d-li14/efficientnetv2.pytorch
apple/ml-mobileone
osmr/imgclsmob
huggingface/pytorch-image-models
moskomule/senet.pytorch
DingXiaoH/RepLKNet-pytorch
huawei-noah/Efficient-AI-Backbones

Misc

torchvision dataset implementations: pytorch/vision
MLP implementation: aaron-xichen/pytorch-playground
AutoAugment implementation: DeepVoltaire/AutoAugment
Cutout implementation: uoguelph-mlrg/Cutout
Robustness measurement image distortions: hendrycks/robustness
Registry implementation: openvinotoolkit/openvino/tools/pot