Connecting with TorchVision's model banks

This release adds an example to showcase how to use the model banks provided by TorchVision to build a new YOLOv5 model, introduces Visualizer to display the predictions and fixes an incompatible bug in YOLOv5 Detect Layer.

Highlights

Build a new YOLOv5 model via TorchVision's model banks

We add an example to showcase how to construct a YOLOv5-Lite model with TorchVision's pre-trained MobileNetV3-Small FPN as the backbone.

import torch
from yolort.models.yolo_lite import yolov5_mobilenet_v3_small_fpn

model = yolov5_mobilenet_v3_small_fpn()
model = model.eval()

images = torch.rand(4, 3, 640, 640)
out = model(images)

• Use C3 following the model specification of YOLOv5 (#343)
• Construct YOLOv5 models with TorchVision MobileNetV3 backbone (#342)

Introduce a new visualization interface

from torchvision.io import read_image
from yolort.models import yolov5n6
from yolort.utils import Visualizer

model = yolov5n6(pretrained=True, score_thresh=0.45)
model = model.eval()
img_path = "bus.jpg"
preds = model.predict(img_path)

metalabels_path = "coco.names"
image = read_image(img_path)
v = Visualizer(image, metalabels=metalabels_path)
v.draw_instance_predictions(preds[0])
v.imshow(scale=0.5)

• Add Visualizer for visualization (#341)

Bugfixes

• Fix YOLOv5 AnchorGenerator compatibility (#345)
• Add missing version param in export_tensorrt (#335)

Code Quality

• Update Visualizer in docs and PR Recommendations (#346)
• Bump versions on GH Action (#344, #338, #337)

Contributors

We're grateful for our community, which helps us improving yolort by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

@Luwill6, @zhiqwang

Full Changelog: v0.6.0...v0.6.1