Replication Data for: Training Deep Convolutional Object Detectors for Images Affected by Lossy Compression
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This collection contains the trained models and object detectionresults of 2 architectures found in the Detectron2 library, on the MSCOCO val2017 dataset, under different JPEG compresion level Q = {5, 12,19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96} (14 levels per trainedmodel).Architectures:F50 – Faster R-CNN on ResNet-50 with FPNR50 – RetinaNet on ResNet-50 with FPNTraining type:D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations,batch 16)STD – standard 1x training (90.000 iterations) on original train2017datasetQ20 – 1x training (90.000 iterations) on train2017 dataset degradedto Q=20Q40 – 1x training (90.000 iterations) on train2017 dataset degradedto Q=40T20 – extra 1x training on top of D2 on train2017 dataset degradedto' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["deep learning","jpeg compression","model training","object detection"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This collection contains the trained models and object detectionresults of 2 architectures found in the Detectron2 library, on the MSCOCO val2017 dataset, under different JPEG compresion level Q = {5, 12,19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96} (14 levels per trainedmodel).Architectures:F50 – Faster R-CNN on ResNet-50 with FPNR50 – RetinaNet on ResNet-50 with FPNTraining type:D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations,batch 16)STD – standard 1x training (90.000 iterations) on original train2017datasetQ20 – 1x training (90.000 iterations) on train2017 dataset degradedto Q=20Q40 – 1x training (90.000 iterations) on train2017 dataset degradedto Q=40T20 – extra 1x training on top of D2 on train2017 dataset degradedto' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This collection contains the trained models and object detectionresults of 2 architectures found in the Detectron2 library, on the MSCOCO val2017 dataset, under different JPEG compresion level Q = {5, 12,19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96} (14 levels per trainedmodel).Architectures:F50 – Faster R-CNN on ResNet-50 with FPNR50 – RetinaNet on ResNet-50 with FPNTraining type:D2 – Detectron2 Model ZOO pre-trained 1x model (90.000 iterations,batch 16)STD – standard 1x training (90.000 iterations) on original train2017datasetQ20 – 1x training (90.000 iterations) on train2017 dataset degradedto Q=20Q40 – 1x training (90.000 iterations) on train2017 dataset degradedto Q=40T20 – extra 1x training on top of D2 on train2017 dataset degradedto
deep learning; jpeg compression; model training; object detection