Downloads · 30 days
0
TCSR
wodanile/TCSR
TCSR is a object detection model from wodanile. Use it when you need objects located in an image. It is set up for pytorch. The card lists the license as other.
This repository contains the public VisDrone checkpoints for Shallow-Response Calibration for Real-Time Aerial Small Object Detection. The implementation, configurations, training commands, and evaluation tools are ma…
Access
Public
Updated Aug 31, 2026
Repo size
5.4 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pth5.4 GB · 100%
At a glance
- Task
- Object Detection
- Library
- pytorch
- License
- other
- Access
- Public
- Created
- Aug 26, 2026
- Updated
- Aug 31, 2026
- SHA
- 1dbebf77
Model card
From the Hugging Face model README
TCSR VisDrone Checkpoints
This repository contains the public VisDrone checkpoints for Shallow-Response Calibration for Real-Time Aerial Small Object Detection. The implementation, configurations, training commands, and evaluation tools are maintained in the TCSR GitHub repository.
Because of storage and long-term maintenance constraints, the hosted checkpoint collection is limited to VisDrone. The AI-TOD-v2 experiment configurations remain available in the GitHub repository.
Available Checkpoints
| Framework | Backbone | Variant | VisDrone AP | AP gain | File |
|---|---|---|---|---|---|
| D-FINE | HGNetv2-S | Baseline | 27.56 | - | dfine_hgnetv2_s_visdrone_baseline_ap=27.56.pth |
| D-FINE | HGNetv2-S | Direct | 28.51 | +0.95 | dfine_hgnetv2_s_visdrone_detection_direct_hard_ap=28.51.pth |
| D-FINE | HGNetv2-S | TCSR-Light | 29.11 | +1.55 | dfine_hgnetv2_s_visdrone_TCSR_light=64_ap=29.11.pth |
| D-FINE | HGNetv2-S | TCSR-Hard | 30.71 | +3.15 | dfine_hgnetv2_s_visdrone_detection_TCSR_hard_ap=30.71.pth |
| RT-DETRv2 | HGNetv2-X | Baseline | 26.52 | - | rtdetrv2_hgnetv2_x_visdrone_baseline_72e_seed0_ap=26.52.pth |
| RT-DETRv2 | HGNetv2-X | SE-Direct | 28.07 | +1.55 | rtdetrv2_hgnetv2_x_visdrone_se_direct_72e_seed0_ap=28.07.pth |
| RT-DETRv2 | HGNetv2-X | TCSR-Hard (deterministic) | 29.37 | +2.85 | rtdetrv2_hgnetv2_x_visdrone_tcsr_det_72e_seed=0_ap=29.37.pth |
| RT-DETRv2 | HGNetv2-X | TCSR-Hard | 29.30 | +2.78 | rtdetrv2_hgnetv2_x_visdrone_TCSR_72e_seed0_AP=29.30.pth |
| RT-DETRv2 | ResNet-18 | Baseline | 29.40 | - | rtdetrv2_r18vd_visdrone_baseline_200e_seed0_ap=29.40.pth |
| RT-DETRv2 | ResNet-18 | Direct | 29.76 | +0.36 | rtdetrv2_r18vd_visdrone_direct_200e_seed0_ap=29.76.pth |
| RT-DETRv2 | ResNet-18 | TCSR-Hard | 31.17 | +1.77 | rtdetrv2_r18vd_200e_visdrone_TCSR_hard=256_ap=31.17.pth |
The table lists the checkpoint files currently hosted in this repository. The revised paper reports the complete RT-DETRv2/ResNet-18 sequence: Baseline 29.40 AP, Direct 29.76 AP (+0.36), TCSR-Light 30.13 AP (+0.73), and TCSR-Hard 31.17 AP (+1.77). Results for additional variants are summarized in the TCSR GitHub repository.
For RT-DETRv2/HGNetv2-X, SE-Direct is the matched global channel-recalibration control. It uses standard squeeze-and-excitation after C2 alignment, a reduction ratio of 16, no stochastic response exposure, the 72-epoch schedule, automatic mixed precision, and seed 0. Its matching configuration is RT-DETR/rtdetrv2_pytorch/configs/rtdetrv2/rtdetrv2_hgnetv2_x_visdrone_se_direct.yml in the GitHub repository.
The deterministic TCSR-Hard checkpoint retains the complete 384-channel local TCSR transformation but disables stochastic response exposure (tcsr_exposure_std: 0.0). It uses the same 72-epoch HGNetv2-X setup, automatic mixed precision, and seed 0; its matching GitHub configuration is RT-DETR/rtdetrv2_pytorch/configs/rtdetrv2/rtdetrv2_hgnetv2_x_visdrone_tcsr_det.yml.
Usage
Download one checkpoint with the Hugging Face CLI:
hf download wodanile/TCSR <checkpoint-name>.pth --local-dir ./checkpoints
Refer to the GitHub repository for the matching configuration and use the framework's evaluation or resume option when loading a trained VisDrone checkpoint. The RT-DETRv2 and D-FINE loaders include compatibility remapping for checkpoints produced before the public TCSR namespace cleanup.
Important Notes
- These are PyTorch checkpoint files. Load checkpoints only from sources you trust.
- Checkpoint filenames record the framework, backbone, variant, schedule or seed where applicable, and reported validation AP.
- The COCO initialization checkpoint
rtdetrv2_r18vd_120e_coco_rerun_48.1.pthis not part of this repository.
- Task
- Object Detection
- Library
- pytorch
- License
- other
- Created
- Aug 26, 2026
- Updated
- Aug 31, 2026