detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of facebook/detr-resnet-50 on cppe5 dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and testing data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Accumulating evaluation results...
DONE (t=0.02s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.272
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.504
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.254
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.131
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.300
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.264
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.446
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.143
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.265
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.516
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 0
Model tree for ARG-NCTU/detr-resnet-50_finetuned_cppe5
Base model
facebook/detr-resnet-50