Feat: Add Model RF-DETR

[DerrickUnleashed]

This PR adds :

• Real-Time Detection Transformers Model (Object Detection Model)
• Test Suite for the same

Closes #327

[DerrickUnleashed]

=======================================
Running inference with: model_rfdetr_base
========================================
Model weights for <rfdetr_base> (~123 MB) will be downloaded and processed if
not already available.
Loaded pretrained weights for <rfdetr_base> (487/487 keys, 0 skipped).
Top detections:
  score=0.8337 label=17 box=[156,37,1440,1148]
  score=0.1323 label=18 box=[156,37,1440,1148]
  score=0.0865 label=23 box=[156,37,1440,1148]
  score=0.0846 label=20 box=[156,37,1440,1148]
  score=0.0698 label=18 box=[164,23,1440,1148]
Detections above 0.3: 1

========================================
Running inference with: model_rfdetr_base_2
========================================
Model weights for <rfdetr_base_2> (~123 MB) will be downloaded and processed
if not already available.
Loaded pretrained weights for <rfdetr_base_2> (487/487 keys, 0 skipped).
Top detections:
  score=0.6605 label=17 box=[156,38,1440,1152]
  score=0.2869 label=18 box=[156,38,1440,1152]
  score=0.1364 label=23 box=[156,38,1440,1152]
  score=0.0740 label=20 box=[156,38,1440,1152]
  score=0.0676 label=16 box=[156,38,1440,1152]
Detections above 0.3: 1

========================================
Running inference with: model_rfdetr_base_o365
========================================
Model weights for <rfdetr_base_o365> (~127 MB) will be downloaded and
processed if not already available.
Loaded pretrained weights for <rfdetr_base_o365> (487/487 keys, 0 skipped).
Top detections:
  score=0.5500 label=140 box=[158,34,1440,1144]
  score=0.4891 label=93 box=[158,34,1440,1144]
  score=0.1077 label=343 box=[158,34,1440,1144]
  score=0.0971 label=321 box=[158,34,1440,1144]
  score=0.0838 label=84 box=[158,34,1440,1144]
Detections above 0.3: 2

========================================
Running inference with: model_rfdetr_large
========================================
Model weights for <rfdetr_large> (~518 MB) will be downloaded and processed
if not already available.
Loaded pretrained weights for <rfdetr_large> (533/533 keys, 0 skipped).
Top detections:
  score=0.9274 label=18 box=[156,40,1444,1136]
  score=0.0538 label=18 box=[163,36,1440,1137]
  score=0.0460 label=19 box=[156,40,1444,1136]
  score=0.0403 label=17 box=[163,36,1440,1137]
  score=0.0336 label=11 box=[156,40,1444,1136]
Detections above 0.3: 1

========================================
Running inference with: model_rfdetr_medium
========================================
Model weights for <rfdetr_medium> (~116 MB) will be downloaded and processed
if not already available.
Loaded pretrained weights for <rfdetr_medium> (465/465 keys, 0 skipped).
Top detections:
  score=0.3381 label=17 box=[155,35,1460,1158]
  score=0.2565 label=18 box=[155,35,1460,1158]
  score=0.1775 label=23 box=[155,35,1460,1158]
  score=0.1350 label=23 box=[157,34,1461,1156]
  score=0.0925 label=17 box=[157,34,1461,1156]
Detections above 0.3: 1

========================================
Running inference with: model_rfdetr_nano
========================================
Model weights for <rfdetr_nano> (~116 MB) will be downloaded and processed if
not already available.
Loaded pretrained weights for <rfdetr_nano> (465/465 keys, 0 skipped).
Top detections:
  score=0.3381 label=17 box=[155,35,1460,1158]
  score=0.2565 label=18 box=[155,35,1460,1158]
  score=0.1775 label=23 box=[155,35,1460,1158]
  score=0.1350 label=23 box=[157,34,1461,1156]
  score=0.0925 label=17 box=[157,34,1461,1156]
Detections above 0.3: 1

========================================
Running inference with: model_rfdetr_small
========================================
Model weights for <rfdetr_small> (~116 MB) will be downloaded and processed
if not already available.
Loaded pretrained weights for <rfdetr_small> (465/465 keys, 0 skipped).
Top detections:
  score=0.5835 label=17 box=[154,34,1459,1155]
  score=0.4582 label=18 box=[154,34,1459,1155]
  score=0.3348 label=23 box=[154,34,1459,1155]
  score=0.0870 label=64 box=[154,34,1459,1155]
  score=0.0798 label=20 box=[154,34,1459,1155]
Detections above 0.3: 3

[DerrickUnleashed]

Can we implement a vignette for this perhaps? @cregouby

[cregouby]

praise This is massive, thanks for it
todo see inline.

[DerrickUnleashed]

url <- "https://upload.wikimedia.org/wikipedia/commons/6/6f/Toy_Poodle_wearing_clothes_in_Tokyo.jpg"
img <- base_loader(url) %>%
  transform_to_tensor()

model <- model_rfdetr_large(pretrained = TRUE)
model$eval()

pred <- torch::with_no_grad(
  model(img$unsqueeze(1))
)$detections[[1]]

topk <- pred$scores$topk(k = 5L)[[2]]
boxes <- pred$boxes[topk, ]
labels <- coco_classes(as.integer(pred$labels[topk]))
canvas <- (img * 255)$to(dtype = torch_uint8())
boxed <- draw_bounding_boxes(canvas, boxes, labels = labels,
 colors = "black", width = 6)
tensor_image_browse(boxed)