Could you provide the fine-tuned weights?

I finetuned your base-class weight on MS COCO dataset using RTX4070ti after 100 epochs with the batch size of 1 while other hyperparamter settings remain the same in accordance with the scripts you provided, but it does not reproduce to the accuracy to your essay out of the blue. The evaluation results for 10 shots are as follows:

Averaged stats: class_error: 76.92 loss: 25.7722 (22.3666) loss_ce: 2.4055 (1.9647) loss_bbox: 0.9593 (0.8582) loss_giou: 1.2056 (1.0262) loss_ce_0: 2.3303 (1.9005) loss_bbox_0: 0.8101 (0.7742) loss_giou_0: 1.0498 (1.0038) loss_ce_1: 2.3550 (1.8903) loss_bbox_1: 0.8683 (0.8341) loss_giou_1: 1.0159 (0.9948) loss_ce_2: 2.4136 (1.9047) loss_bbox_2: 0.8322 (0.8132) loss_giou_2: 1.0873 (0.9953) loss_ce_3: 2.2909 (1.8730) loss_bbox_3: 0.8844 (0.8044) loss_giou_3: 1.0809 (1.0009) loss_ce_4: 2.1820 (1.9072) loss_bbox_4: 0.8562 (0.8144) loss_giou_4: 1.0902 (1.0071) loss_ce_unscaled: 1.2028 (0.9824) class_error_unscaled: 50.0000 (43.5332) loss_bbox_unscaled: 0.1919 (0.1716) loss_giou_unscaled: 0.6028 (0.5131) cardinality_error_unscaled: 299.5000 (299.4213) loss_ce_0_unscaled: 1.1651 (0.9503) loss_bbox_0_unscaled: 0.1620 (0.1548) loss_giou_0_unscaled: 0.5249 (0.5019) cardinality_error_0_unscaled: 298.4375 (298.3096) loss_ce_1_unscaled: 1.1775 (0.9451) loss_bbox_1_unscaled: 0.1737 (0.1668) loss_giou_1_unscaled: 0.5080 (0.4974) cardinality_error_1_unscaled: 299.3750 (299.0845) loss_ce_2_unscaled: 1.2068 (0.9523) loss_bbox_2_unscaled: 0.1664 (0.1626) loss_giou_2_unscaled: 0.5437 (0.4976) cardinality_error_2_unscaled: 298.7500 (298.6726) loss_ce_3_unscaled: 1.1455 (0.9365) loss_bbox_3_unscaled: 0.1769 (0.1609) loss_giou_3_unscaled: 0.5405 (0.5004) cardinality_error_3_unscaled: 299.3125 (299.0845) loss_ce_4_unscaled: 1.0910 (0.9536) loss_bbox_4_unscaled: 0.1712 (0.1629) loss_giou_4_unscaled: 0.5451 (0.5035) cardinality_error_4_unscaled: 299.4375 (299.2578)

Novel Categories: Accumulating evaluation results... DONE (t=1.25s). IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.075 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.163 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.061 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.015 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.061 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.127 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.213 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.222 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.043 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.160 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.360

Thus, I'm wondering if you could provide your fine-tuned weights, and my email address is 1819409756@qq.com.

Looking forward to your reply.

ZhangGongjie / Meta-DETR

Could you provide the fine-tuned weights? #72