vision_transformers

A repository for everything Vision Transformers.

Currently Supported Models

Image Classification
- ViT Base Patch 16 | 224x224: Torchvision pretrained weights
- ViT Base Patch 32 | 224x224: Torchvision pretrained weights
- ViT Tiny Patch 16 | 224x224: Timm pretrained weights
- Vit Tiny Patch 16 | 384x384: Timm pretrained weights
- Swin Transformer Tiny Patch 4 Window 7 | 224x224: Official Microsoft weights
- Swin Transformer Small Patch 4 Window 7 | 224x224: Official Microsoft weights
- Swin Transformer Base Patch 4 Window 7 | 224x224: Official Microsoft weights
- Swin Transformer Large Patch 4 Window 7 | 224x224: No pretrained weights
- MobileViT S
- MobileViT XS
- MobileVit XXS
Object Detection
- DETR ResNet50 (COCO pretrained)
- DETR ResNet50 DC5 (COCO pretrained)
- DETR ResNet101 (COCO pretrained)
- DETR ResNet101 DC5 (COCO pretrained)

GO TO

Quick Setup
Importing Models and Usage
DETR Video Inference Commands (COCO pretrained models)
Examples

Quick Setup

Stable PyPi Package

pip install vision-transformers

OR

Latest Git Updates

git clone https://github.com/sovit-123/vision_transformers.git
cd vision_transformers

Installation in the environment of your choice:

pip install .

Importing Models and Usage

If you have you own training pipeline and just want the model

Replace num_classes=1000 with you own number of classes.

from vision_transformers.models import vit

model = vit.vit_b_p16_224(num_classes=1000, pretrained=True)
# model = vit.vit_b_p32_224(num_classes=1000, pretrained=True)
# model = vit.vit_ti_p16_224(num_classes=1000, pretrained=True)

from vision_transformers.models import swin_transformer

model = swin_transformer.swin_t_p4_w7_224(num_classes=1000, pretrained=True)
# model = swin_transformer.swin_s_p4_w7_224(num_classes=1000, pretrained=True)
# model = swin_transformer.swin_b_p4_w7_224(num_classes=1000, pretrained=True)
# model = swin_transformer.swin_l_p4_w7_224(num_classes=1000)

If you want to use the training pipeline

Clone the repository:

git clone https://github.com/sovit-123/vision_transformers.git
cd vision_transformers

Install

pip install .

From the vision_transformers directory:

If you have no validation split

python tools/train_classifier.py --data data/diabetic_retinopathy/colored_images/ 0.15 --epochs 5 --model vit_ti_p16_224

In the above command:
- data/diabetic_retinopathy/colored_images/ represents the data folder where the images will be inside the respective class folders
- 0.15 represents the validation split as the dataset does not contain a validation folder
If you have validation split

python tools/train_classifier.py --train-dir data/plant_disease_recognition/train/ --valid-dir data/plant_disease_recognition/valid/ --epochs 5 --model vit_ti_p16_224

In the above command:
- --train-dir should be path to the training directory where the images will be inside their respective class folders.
- --valid-dir should be path to the validation directory where the images will be inside their respective class folders.

All Available Model Flags for `--model`

vit_b_p32_224
vit_ti_p16_224
vit_ti_p16_384
vit_b_p16_224
swin_b_p4_w7_224
swin_t_p4_w7_224
swin_s_p4_w7_224
swin_l_p4_w7_224
mobilevit_s
mobilevit_xs
mobilevit_xxs

DETR Training

The datasets annotations should be in XML format. The dataset (according to --data flag) given in following can be found here => https://www.kaggle.com/datasets/sovitrath/aquarium-data

python tools/train_detector.py --model detr_resnet50 --epochs 2 --data data/aquarium.yaml

DETR Image Inference (using trained weights)

Replace weights and input file path as per your requirement.

python tools/inference_image_detect.py --weights runs/training/res_1/best_model.pth --input image.jpg

You can also provide the path to a directory to run inference on all images in that directory.

python tools/inference_image_detect.py --weights runs/training/res_1/best_model.pth --input image_directory

DETR Video Inference (using trained weights)

Replace weights and input file path as per your requirement. You can add --show to the command to visualize the detection on screen.

python tools/inference_video_detect.py --weights runs/training/res_1/best_model.pth --input video.mp4

DETR Video Inference Commands (COCO pretrained models)

All commands to be executed from the root project directory (vision_transformers)

python tools/inference_video_detect.py --model detr_resnet50 --show --input example_test_data/video_1.mp4
                                               detr_resnet50_dc5            <path/to/your/file>
                                               detr_resnet101               
                                               detr_resnet101_dc5

Tracking using COCO Pretrained Weights

# Track all COCO classes.
python tools/inference_video_detect.py --track --model detr_resnet50 --show --input example_test_data/video_1.mp4
                                                       detr_resnet50_dc5            <path/to/your/file>
                                                       detr_resnet101               
                                                       detr_resnet101_dc5

# Track only person class (for DETR, object indices start from 2 for COCO pretrained models). Check `data/test_video_config.yaml` for more information.
python tools/inference_video_detect.py --track --model detr_resnet50 --show --input ../inference_data/video_4.mp4 --classes 2

# Track person and motocycle classes (for DETR, object indices start from 2 for COCO pretrained models). Check `data/test_video_config.yaml` for more information.
python tools/inference_video_detect.py --track --model detr_resnet50 --show --input ../inference_data/video_4.mp4 --classes 2 5

Tracking using Custom Trained Weights

Just provide the path to the trained weights instead of a model.

python tools/inference_video_detect.py --track --weights runs/training/res_1/best_model.pth --show --input ../inference_data/video_4.mp4

Examples

ViT Base 16 | 224x224 pretrained fine-tuning on CIFAR10
ViT Tiny 16 | 224x224 pretrained fine-tuning on CIFAR10
DETR image inference notebook
DETR video inference script (Fine Tuning Coming Soon) --- Check commands here

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