United-Perception
United Perception
파일 탐색기
최종 버전 다운로드 (.zip)- 3d_detection_benchmark.md
- bignas_supernet.md
- classification_benchmark.md
- detection_benchmark.md
- distillation.md
- equalized_focal_loss.md
- multitask_benchmark.md
- quant_benchmark.md
- semantic_benchmark.md
- sparse_benchmark.md
- ssl_benchmark.md
- convnext_s.yaml
- convnext_t.yaml
- cswin_base.yaml
- cswin_small.yaml
- cswin_tiny.yaml
- inference_from_file_or_dir.yaml
- mbv2_0.5_batch1k_epoch250_coslr_nesterov_wd0.00004_bn_nowd_fp16_ema.yaml
- mbv2_1.0_batch1k_epoch250_coslr_nesterov_wd0.00004_bn_nowd_fp16_ema.yaml
- mbv2_2.0_batch1k_epoch250_coslr_nesterov_wd0.00004_bn_nowd_fp16_ema.yaml
- mbv3_large_1.0_batch1k_epoch350_coslr_nesterov_wd0.00003_bn_nowd_fp16_ema0.9999_dropout0.2.yaml
- mbv3_small_1.0_batch1k_epoch350_coslr_nesterov_wd0.00003_bn_nowd_fp16_ema0.9999_dropout0.2.yaml
- res18.yaml
- reg_x200.yaml
- reg_x400.yaml
- reg_y200.yaml
- reg_y400.yaml
- res50_car.yaml
- res50_car_strikes.yaml
- res50_flower.yaml
- res50_flower_strikes.yaml
- res50_pet.yaml
- res18.yaml
- res18_200e_bag_of_tricks.yaml
- res18_strikes_100e_bce.yaml
- res18_strikes_300e_bce.yaml
- res34.yaml
- res50.yaml
- res50_200e_bag_of_tricks.yaml
- res50_grad_cam_show.yaml
- res50_softmax_eql.yaml
- res50_strikes_100e_bce.yaml
- res50_strikes_300e_bce.yaml
- resnet50D_bag_of_tricks.yaml
- swin_tiny_batch.yaml
- swin_transformer_base_224.yaml
- swin_transformer_base_384.yaml
- swin_transformer_large_224.yaml
- swin_transformer_large_384.yaml
- swin_transformer_small.yaml
- swin_transformer_tiny.yaml
- vit_b16_224.yaml
- cascade-rcnn-R50-FPN-1x.yaml
- condinst-R50-1x.yaml
- voc_face_cross.yaml
- custom_dataset.yaml
- custom_dataset_inference.yaml
- rank_dataset.yaml
- rank_dataset_inference.yaml
- efl_improved_baseline_r101_2x_rfs.yaml
- efl_improved_baseline_r50_2x_rfs.yaml
- efl_oids_r101_2x_random.yaml
- efl_oids_r50_2x_random.yaml
- efl_yolox_medium.yaml
- efl_yolox_small.yaml
- eqfl_yolox_medium.yaml
- eqfl_yolox_samll.yaml
- faster_rcnn_r50_C4_1x.yaml
- faster_rcnn_r50_fpn_1x.yaml
- faster_rcnn_r50_fpn_improve.yaml
- fcos-R50-1x.yaml
- onenet-r18.yaml
- onenet-r50_3x.yaml
- retinanet-r18-improve.yaml
- retinanet-r18-improve_cos_iou.yaml
- retinanet-r18-improve_ota.yaml
- retinanet-r18_mosiac_cos_ema_iou.yaml
- retinanet-r50_1x.yaml
- retinanet-r50_1x_fp16.yaml
- retinanet-xmnet-improve.yaml
- yolox_l_ret_a1_comloc.yaml
- yolox_m_ret_a1_comloc.yaml
- yolox_m_ret_a2_comloc.yaml
- yolox_n_ret_a1_comloc.yaml
- yolox_n_ret_a2_comloc.yaml
- yolox_n_ret_a2_comloc_ada.yaml
- yolox_s_ret_a1_comloc.yaml
- yolox_s_ret_a2_comloc.yaml
- yolox_t_ret_a1_comloc.yaml
- yolox_t_ret_a2_comloc.yaml
- yolox_t_ret_a2_comloc_ada.yaml
- yolox_x_ret_a1_comloc.yaml
- rfcn-R101-1x.yaml
- rfcn-R50-1x.yaml
- anchors.json
- yolov5_small_relu.yaml
- yolov5_small_silu.yaml
- yolox_v6n_ret_a2_comloc_400e_640x640.yaml
- yolox_v6n_ret_a2_comloc_400e_640x640_v6aug.yaml
- yolox_v6s_ret_a2_comloc_400e_640x640.yaml
- yolox_v6s_ret_a2_comloc_400e_640x640_v6aug.yaml
- yolox_v6t_ret_a2_comloc_400e_640x640.yaml
- yolox_v6t_ret_a2_comloc_400e_640x640_v6aug.yaml
- yolox_fpga.yaml
- yolox_large.yaml
- yolox_medium.yaml
- yolox_nano.yaml
- yolox_small.yaml
- yolox_small_res18.yaml
- yolox_tiny.yaml
- yolox_x.yaml
- centerpoint_pillar.yaml
- centerpoint_second.yaml
- anchors.json
- pointpillar.yaml
- anchors.json
- second.yaml
- res18_kd.yaml
- res18_kd_bag_of_tricks.yaml
- faster_rcnn_r152_50_1x_decouple_feature_mimic.yaml
- faster_rcnn_r152_50_1x_feature_mimic.yaml
- faster_rcnn_r152_50_1x_frs.yaml
- faster_rcnn_r152_50_1x_multi_jobs_multi_teacheres.yaml
- faster_rcnn_r152_50_1x_sample_feature_mimic.yaml
- r50-retina-atss-qfl+cls.yaml
- r50-retina-atss-qfl.yaml
- bignas_regnet_800m_sample_flops.yaml
- bignas_regnet_800m_train_supnet.yaml
- bignas_retinanet_R18_evaluate_subnet.yaml
- bignas_retinanet_R18_finetune_subnet.yaml
- bignas_retinanet_R18_sample_accuracy.yaml
- bignas_retinanet_R18_sample_flops.yaml
- bignas_retinanet_R18_subnet.yaml
- bignas_retinanet_R18_train_supnet.yaml
- retinanet-xmnet.yaml
- res18_quant_trt_qat.yaml
- res18_quant_trt_qat.yaml
- faster_rcnn_r18_FPN_2x_quant_qdrop.yaml
- faster_rcnn_r50_fpn_improve_quant_trt_qat.yaml
- faster_rcnn_r50_fpn_improve_quant_trt_qat_deploy.yaml
- retinanet-r18-improve_quant_trt_qat.yaml
- retinanet-r50-w2a4-ada.yaml
- retinanet-r50-w2a4-brecq.yaml
- retinanet-r50-w2a4-qdrop.yaml
- yolox_s_ret_a1_comloc_quant_trt_qat.yaml
- yolox_fpga_quant_vitis_qat.yaml
- deeplabv3_xmnet.yaml
- hrnet18_1024x1024.yaml
- hrnet18_1024x1024_ema.yaml
- hrnet18_small_v1_1024x1024_ema.yaml
- hrnet18_small_v2_1024x1024_ema.yaml
- hrnet48_1024x1024.yaml
- hrnet48_1024x1024_ocr_ema.yaml
- segformer_b0.yaml
- segformer_b1.yaml
- segformer_b2.yaml
- segformer_b3.yaml
- segformer_b4.yaml
- segformer_b5.yaml
- res18_amba_sparse.yaml
- res18_ampere_sparse.yaml
- faster_rcnn_r50_fpn_amba_sparse.yaml
- faster_rcnn_r50_fpn_ampere_sparse.yaml
- retinanet-r18-improve_amba_sparse.yaml
- retinanet-r18-improve_ampere_sparse.yaml
- mae_vit_base_patch16_dec512d8b.yaml
- mae_vit_base_patch16_dec512d8b_800e.yaml
- mae_vit_base_patch16_dec512d8b_finetune.yaml
- mae_vit_base_patch16_dec512d8b_linear.yaml
- moco_v1.yaml
- moco_v1_imagenet_linear.yaml
- moco_v2.yaml
- moco_v2_imagenet_linear.yaml
- moco_v3.yaml
- simclr_v1.yaml
- simclr_v2.yaml
- simsiam_100e.yaml
- simsiam_linear.yaml
- convert.py
- index.rst
- index.rst
- index.rst
- 3ddet.rst
- bignas.rst
- cls.rst
- det.rst
- distill.rst
- multitask.rst
- quant.rst
- seg.rst
- sparse.rst
- ssl.rst
- index.rst
- augmentations.rst
- configs.rst
- datasets.rst
- environment.rst
- fp16.rst
- loss.rst
- model_flow.rst
- normalization.rst
- quant.rst
- saver.rst
- trainer.rst
- index.rst
- index.rst
- index.rst
- index.rst
- index.rst
- logo.png
- index.rst
- index.rst
- index.rst
- 3ddet.rst
- bignas.rst
- cls.rst
- det.rst
- distill.rst
- multitask.rst
- quant.rst
- seg.rst
- sparse.rst
- ssl.rst
- index.rst
- augmentations.rst
- configs.rst
- datasets.rst
- environment.rst
- fp16.rst
- loss.rst
- model_flow.rst
- normalization.rst
- quant.rst
- saver.rst
- sparse.rst
- trainer.rst
- index.rst
- index.rst
- index.rst
- index.rst
- index.rst
- logo.png
- conf.py
- index.rst
- logo.png
- make.bat
- Makefile
- dist_inference.sh
- dist_test.sh
- dist_train.sh
- flops.sh
- inference.sh
- qat_deploy.sh
- qat_deploy_dist_pytorch.sh
- test.sh
- to_onnx.sh
- train.sh
- train_ptq.sh
- train_qat.sh
- train_qat_dist_pytorch.sh
- train_sparse.sh
- __init__.py
- inference.py
- __init__.py
- eval.py
- flops.py
- inference.py
- inference_video.py
- quant_deploy.py
- subcommand.py
- to_onnx.py
- train.py
- __init__.py
- base_dataset.py
- transforms.py
- __init__.py
- base_evaluator.py
- __init__.py
- batch_sampler.py
- sampler.py
- __init__.py
- data_builder.py
- data_loader.py
- data_utils.py
- image_reader.py
- cross_focal_loss.h
- cross_focal_loss_cuda.cpp
- cross_focal_loss_sigmoid_kernel.cu
- deform_conv.cpp
- deform_conv_cuda.cpp
- deform_conv_cuda_kernel.cu
- deformable_conv.h
- focal_loss.h
- focal_loss_cuda.cpp
- focal_loss_sigmoid_kernel.cu
- focal_loss_softmax_kernel.cu
- iou3d_cpu.cpp
- iou3d_cpu.h
- iou3d_nms.cpp
- iou3d_nms.h
- iou3d_nms_api.cpp
- iou3d_nms_kernel.cu
- iou_overlap.cpp
- iou_overlap.h
- iou_overlap_kernel.cu
- nms.h
- nms_cuda.cpp
- nms_kernel.cu
- psroi_align.h
- psroi_align_cuda.cpp
- psroi_align_kernel.cu
- psroi_pooling.cpp
- psroi_pooling.h
- psroi_pooling_cuda.cpp
- psroi_pooling_kernel.cu
- roi_align.cpp
- roi_align.h
- roi_align_cuda.cpp
- roi_align_kernel.cu
- roiaware_pool3d.cpp
- roiaware_pool3d.h
- roiaware_pool3d_kernel.cu
- roipoint_pool3d.cpp
- roipoint_pool3d.h
- roipoint_pool3d_kernel.cu
- softer_nms.cpp
- softer_nms.h
- pybind.cpp
- cross_focal_loss.py
- deformable_conv.py
- focal_loss.py
- iou3d_nms_utils.py
- iou_overlap.py
- nms.py
- psroi_align.py
- psroi_pool.py
- roi_align.py
- roiaware_pool3d_utils.py
- roipoint_pool3d_utils.py
- __init__.py
- ext.py
- __init__.py
- convnext.py
- cswin.py
- efficientnet.py
- mae_vit.py
- mobilenet_v2.py
- mobilenet_v3.py
- moco_vit.py
- regnet.py
- resnet.py
- resnet_D.py
- swin_transformer.py
- vision_transformer.py
- __init__.py
- loss.py
- __init__.py
- model_helper.py
- __init__.py
- base_runner.py
- __init__.py
- auto_augmentation.py
- cls_dataloader.py
- cls_dataset.py
- cls_evaluator.py
- cls_transforms.py
- data_utils.py
- rand_augmentation.py
- __init__.py
- cls_head.py
- __init__.py
- cls_loss.py
- __init__.py
- cls_postprocess.py
- __init__.py
- __init__.py
- __init__.py
- coco_dataset.py
- custom_dataset.py
- det_transforms.py
- lvis_dataset.py
- __init__.py
- coco_evaluator.py
- custom_evaluator.py
- eval_utils.py
- group_evaluator.py
- lvis_evaluator.py
- __init__.py
- __init__.py
- bbox_head.py
- __init__.py
- retina_head.py
- __init__.py
- __init__.py
- entropy_loss.py
- focal_loss.py
- iou_loss.py
- l1_loss.py
- ohem.py
- smooth_l1_loss.py
- __init__.py
- fpn.py
- __init__.py
- bbox_post_process.py
- bbox_predictor.py
- bbox_supervisor.py
- retina_post_process.py
- roi_predictor.py
- roi_supervisor.py
- __init__.py
- anchor_generator.py
- assigner.py
- bbox_helper.py
- box_sampler.py
- matcher.py
- nms_wrapper.py
- __init__.py
- __init__.py
- bbox.py
- bbox_head.py
- __init__.py
- __init__.py
- __init__.py
- condinst_head.py
- __init__.py
- condinst_postprocess.py
- condinst_predictor.py
- condinst_supervisor.py
- __init__.py
- __init__.py
- __init__.py
- efl.py
- eqfl.py
- __init__.py
- __init__.py
- optimizer_helper.py
- __init__.py
- __init__.py
- fcos_head.py
- __init__.py
- fcos_postprocess.py
- fcos_predictor.py
- fcos_supervisor.py
- __init__.py
- __init__.py
- __init__.py
- transforms.py
- __init__.py
- onenet_postprocess.py
- onenet_predictor.py
- onenet_supervisor.py
- __init__.py
- __init__.py
- __init__.py
- transforms.py
- __init__.py
- darknetv5.py
- __init__.py
- yolov5_head.py
- __init__.py
- yolov5_pan.py
- __init__.py
- roi_predictor.py
- roi_supervisor.py
- yolov5_post_process.py
- __init__.py
- components.py
- initializer.py
- __init__.py
- lr_helper.py
- optimizer_helper.py
- __init__.py
- __init__.py
- efficientrep.py
- __init__.py
- effidehead.py
- __init__.py
- reppan.py
- __init__.py
- components.py
- __init__.py
- __init__.py
- transforms.py
- __init__.py
- cspdarknet.py
- __init__.py
- yolox_head.py
- __init__.py
- pafpn.py
- __init__.py
- roi_predictor.py
- roi_supervisor.py
- yolox_postprocess.py
- __init__.py
- multiscale.py
- __init__.py
- __init__.py
- hook_helper.py
- lr_helper.py
- optimizer_helper.py
- __init__.py
- __init__.py
- __init__.py
- __init__.py
- kitti_dataset.py
- transforms.py
- __init__.py
- eval.py
- evaluate.py
- kitti_common.py
- README.md
- rotate_iou.py
- __init__.py
- kitti_evaluator.py
- product_evaluator.py
- product_object_eval.py
- __init__.py
- box_coder_utils.py
- box_utils.py
- data_loader.py
- data_reader.py
- data_utils.py
- __init__.py
- base_bev_backbone.py
- __init__.py
- mean_vfe.py
- pillar_vfe.py
- __init__.py
- map_to_bev.py
- spconv_backbone.py
- __init__.py
- anchor_head.py
- center_head.py
- __init__.py
- center_loss.py
- __init__.py
- anchor_head_post_process.py
- anchor_head_predictor.py
- anchor_head_supervisor.py
- __init__.py
- center_head_post_process.py
- center_head_predictor.py
- center_head_supervisior.py
- __init__.py
- __init__.py
- anchor_generator.py
- anchor_generator_eagle.py
- center_utils.py
- model_nms_utils.py
- voxel_generator.py
- __init__.py
- __init__.py
- point_runner.py
- __init__.py
- __init__.py
- ce_loss.py
- kl_loss.py
- l2_loss.py
- __init__.py
- mimic_adapt.py
- utils.py
- __init__.py
- kd_runner.py
- __init__.py
- mimicker.py
- __init__.py
- union_fc_cls.py
- union_retina_cls.py
- utils.py
- __init__.py
- multitask_wrapper.py
- wrapper_utils.py
- __init__.py
- __init__.py
- multitask_runner.py
- __init__.py
- __init__.py
- misc.py
- __init__.py
- base_controller.py
- __init__.py
- big_regnet.py
- big_resnet_basic.py
- __init__.py
- big_clshead.py
- big_retinanetwithbn.py
- big_roi_head.py
- __init__.py
- big_fpn.py
- __init__.py
- dynamic_blocks.py
- dynamic_ops.py
- dynamic_utils.py
- normal_blocks.py
- __init__.py
- base_bignas_searchspace.py
- __init__.py
- __init__.py
- bignas_runner.py
- __init__.py
- registry_factory.py
- sample.py
- saver_helper.py
- traverse.py
- __init__.py
- README.md
- __init__.py
- search.py
- __init__.py
- base_ssd.py
- xmnet_ssd.py
- __init__.py
- seg_xmnet.py
- xmnet.py
- xmnet_search.py
- __init__.py
- deeplab.py
- __init__.py
- __init__.py
- __init__.py
- __init__.py
- quant_deploy.py
- __init__.py
- model_helper.py
- __init__.py
- quant_runner.py
- __init__.py
- __init__.py
- seg_dataloader.py
- seg_dataset.py
- seg_evaluator.py
- seg_transfomer.py
- __init__.py
- ocrnet.py
- segformer_decoder.py
- __init__.py
- hrnet.py
- segformer_encoder.py
- __init__.py
- seg_loss.py
- __init__.py
- components.py
- __init__.py
- embed.py
- optimizer_helper.py
- shape_convert.py
- __init__.py
- __init__.py
- cls_head.py
- __init__.py
- model_helper.py
- __init__.py
- sparse_runner.py
- __init__.py
- __init__.py
- ssl_dataset.py
- ssl_transforms.py
- __init__.py
- ssl_loss.py
- __init__.py
- ssl_postprocess.py
- __init__.py
- __init__.py
- mae.py
- moco.py
- simclr.py
- simsiam.py
- __init__.py
- __init__.py
- __init__.py
- analysis_utils.py
- dist_helper.py
- gene_env.py
- launch.py
- __init__.py
- cfg_helper.py
- checkpoint.py
- computation_calculator.py
- context.py
- dc_manager.py
- fake_linklink.py
- fp16_helper.py
- global_flag.py
- hook_helper.py
- log_helper.py
- model_load_utils.py
- model_wrapper.py
- petrel_helper.py
- registry.py
- registry_factory.py
- saver_helper.py
- toonnx_helper.py
- user_analysis_helper.py
- vis_helper.py
- yaml_loader.py
- __init__.py
- lamb.py
- lars.py
- __init__.py
- accuracy.py
- act_fn.py
- block_helper.py
- bn_helper.py
- ema_helper.py
- initializer.py
- lr_helper.py
- normalize.py
- optimizer_helper.py
- pos_embed.py
- utils.py
- __init__.py
- __init__.py
- __main__.py
- .flake8
- .gitignore
- easy_setup.sh
- LICENSE
- README.md
- requirements.txt
- setup.py
- up-logo.png
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/ModelTC/United-Perception
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd United-Perception
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Python
쉬움 추천사전 준비물
pip install -r requirements.txt
requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.
python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요
파이썬 스크립트(또는 모듈)를 실행합니다.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
// repository documentation
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