What is the prediction count for the canned demo?

[aurotripathy]

Thank you for your innovative solution to an important problem. Reproducing the "eval" portion, I get a prediction count of 29.15.

Is that what I should expect?

Invocation and output below.

(pyt1.2) auro@auro-ml:~/LearningToCountEverything$ python demo.py --input-image orange.jpg --bbox-file orange_box_ex.txt

Namespace(adapt=False, bbox_file='orange_box_ex.txt', gpu_id=0, gradient_steps=100, input_image='orange.jpg', learning_rate=1e-07, model_path='./data/pretrainedModels/FamNet_Save1.pth', output_dir='.', weight_mincount=1e-09, weight_perturbation=0.0001)

Bounding boxes: [[71, 49, 104, 83], [134, 119, 169, 151], [7, 200, 44, 236]]

/home/auro/anaconda3/envs/pyt1.2/lib/python3.8/site-packages/torch/nn/functional.py:3060: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. warnings.warn("Default upsampling behavior when mode={} is changed "

===> The predicted count is: 29.15 ===> Visualized output is saved to ./orange_out.png

[Viresh-R]

Hey, yes, 29.15 is the expected count (without adaptation). With adaptation, you should get a count of 30.05.