torchvision ResNet

pytorch.org/vision/0.13/models.html has a minimal runnable example adapted to python/pytorch/resnet_demo.py.

That example uses a ResNet pre-trained on the COCO dataset to do some inference, tested on Ubuntu 22.10:

cd python/pytorch
wget -O resnet_demo_in.jpg https://upload.wikimedia.org/wikipedia/commons/thumb/6/60/Rooster_portrait2.jpg/400px-Rooster_portrait2.jpg
./resnet_demo.py resnet_demo_in.jpg resnet_demo_out.jpg

This first downloads the model, which is currently 167 MB.

We know it is COCO because of the docs: pytorch.org/vision/0.13/models/generated/torchvision.models.detection.fasterrcnn_resnet50_fpn_v2.html which explains that

FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT

is an alias for:

FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1

The runtime is relatively slow on P51, about 4.7s.

After it finishes, the program prints the recognized classes:

['bird', 'banana']

so we get the expected bird, but also the more intriguing banana.

By looking at the output image with bounding boxes, we understand where the banana came from!

Figure 7.
python/pytorch/resnet_demo_out.jpg
. The beak was of course a banana, not a beak!

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ResNet implementation