You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

You agree to not use the model for non-commercial use ONLY

rtdetr-sportsmot — RT-DETRv2 (r50vd) fine-tuned on SportsMOT for player detection

Single-class (player) detector fine-tuned from PekingU/rtdetr_v2_r50vd on the annotated train sequences of Lekim89/sportsmot (basketball, soccer, and volleyball broadcast clips in MOTChallenge format). Intended as the detection stage of a tracking-by-detection pipeline (e.g. with ByteTrack via the supervision library) for basketball player tracking.

Try it in one click

Live demo Space — detection in your browser (transformers.js + q8 ONNX, ~45 MB download, no server, images never leave the page): drop a sports frame, get player boxes.
Notebook (notebook.ipynb): open in Colab or Kaggle, or via the "Use this model" button above — install → load → detect → visualize, no token needed, CPU or GPU.

Validation (held-out basketball sequence `v_-6Os86HzwCs_c009`)

metric	value
mAP@[.5:.95]	0.7809
mAP@50	0.9597
mAP@75	0.8700
mAR@100	0.8081

Usage

Note: this is a vision model — its preprocessor is an AutoImageProcessor (there is no tokenizer). Ignore the auto-generated "Load model directly" snippet in the Use this model button if it shows AutoTokenizer — that call will error. Use either option below.

Simplest — pipeline:

from transformers import pipeline

pipe = pipeline("object-detection", model="smallTech/rtdetr-sportsmot")
results = pipe("frame.jpg", threshold=0.5)   # [{'score', 'label': 'player', 'box'}, ...]

Manual — processor + model (full control over pre/post-processing):

import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection

processor = AutoImageProcessor.from_pretrained("smallTech/rtdetr-sportsmot")
model = AutoModelForObjectDetection.from_pretrained("smallTech/rtdetr-sportsmot")

image = Image.open("frame.jpg")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
results = processor.post_process_object_detection(
    outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.5
)

Training details

Approach: RT-DETRv2's COCO detection head is replaced with a one-class (player) head while reusing the pretrained backbone.
Data: the annotated train/ split of SportsMOT — 44 of its 45 sequences for training (14,049 frames at stride 2, frames without boxes skipped), with one basketball sequence (v_-6Os86HzwCs_c009, 100 frames at stride 5) held out for validation. Annotations are MOTChallenge gt.txt boxes; conf==0 ignore-boxes are skipped, track ids are unused for detection. The val/ and test/ splits were never touched by training — they are the benchmark sets below.
Training ran entirely on Kaggle's free tier (single T4, fp16): batch 4 with 4-step gradient accumulation (effective 16), 20 epochs at 640×640, lr 5e-5 cosine with 300-step warmup, augmentation (hflip, color jitter, gaussian noise).
Model selection: checkpoint with the best held-out eval_loss — epoch 11 of 20 (later epochs overfit).

Benchmark — unseen val split (45 sequences)

Evaluated on every annotated frame of the SportsMOT val split — 26,970 frames / 295,573 ground-truth boxes across 45 sequences (basketball, soccer, volleyball) never used in training (training consumed only the train/ split; test/ has no public ground truth). Frame stride 1; same metric implementation as the training-time validation (torchmetrics COCO mAP, detection threshold 0.01).

metric	value
mAP@[.5:.95]	0.7928
mAP@50	0.9383
mAP@75	0.8820
mAR@100	0.8269

Latency (single image, batch 1, fp32, Tesla T4, torch 2.10.0+cu128): mean 61.6 ms · p50 61.4 ms · p90 62.8 ms · p99 66.3 ms → 16.2 fps. Model load: 5.4 s.

Per-sequence results (sorted by mAP)

sequence	mAP@[.5:.95]	mAP@50	mAP@75
`v_4-EmEtrturE_c009`	0.899	0.980	0.959
`v_cC2mHWqMcjk_c009`	0.868	0.970	0.937
`v_cC2mHWqMcjk_c007`	0.859	0.970	0.937
`v_00HRwkvvjtQ_c003`	0.852	0.980	0.934
`v_cC2mHWqMcjk_c008`	0.839	0.959	0.906
`v_4r8QL_wglzQ_c001`	0.835	0.970	0.915
`v_5ekaksddqrc_c005`	0.832	0.969	0.913
`v_G-vNjfx1GGc_c008`	0.825	0.976	0.943
`v_dw7LOz17Omg_c067`	0.823	0.954	0.923
`v_5ekaksddqrc_c002`	0.822	0.979	0.908
`v_5ekaksddqrc_c004`	0.821	0.979	0.908
`v_G-vNjfx1GGc_c004`	0.819	0.964	0.931
`v_dw7LOz17Omg_c053`	0.816	0.958	0.913
`v_5ekaksddqrc_c003`	0.814	0.970	0.900
`v_2QhNRucNC7E_c017`	0.814	0.923	0.902
`v_00HRwkvvjtQ_c001`	0.812	0.960	0.890
`v_0kUtTtmLaJA_c004`	0.810	0.950	0.895
`v_0kUtTtmLaJA_c008`	0.809	0.969	0.882
`v_0kUtTtmLaJA_c010`	0.807	0.970	0.893
`v_ITo3sCnpw_k_c010`	0.804	0.914	0.894
`v_0kUtTtmLaJA_c007`	0.801	0.960	0.868
`v_i2_L4qquVg0_c010`	0.796	0.936	0.905
`v_BgwzTUxJaeU_c014`	0.791	0.948	0.878
`v_i2_L4qquVg0_c007`	0.791	0.921	0.898
`v_ITo3sCnpw_k_c012`	0.790	0.928	0.906
`v_00HRwkvvjtQ_c005`	0.789	0.950	0.870
`v_00HRwkvvjtQ_c008`	0.788	0.959	0.864
`v_BgwzTUxJaeU_c008`	0.781	0.959	0.845
`v_5ekaksddqrc_c001`	0.777	0.960	0.876
`v_0kUtTtmLaJA_c005`	0.776	0.940	0.862
`v_i2_L4qquVg0_c009`	0.774	0.916	0.873
`v_00HRwkvvjtQ_c011`	0.773	0.949	0.855
`v_ITo3sCnpw_k_c011`	0.773	0.915	0.872
`v_0kUtTtmLaJA_c006`	0.771	0.938	0.862
`v_i2_L4qquVg0_c006`	0.766	0.911	0.879
`v_9MHDmAMxO5I_c009`	0.760	0.911	0.844
`v_9MHDmAMxO5I_c002`	0.752	0.940	0.849
`v_00HRwkvvjtQ_c007`	0.752	0.938	0.846
`v_BgwzTUxJaeU_c012`	0.746	0.938	0.841
`v_9MHDmAMxO5I_c003`	0.743	0.910	0.819
`v_ITo3sCnpw_k_c007`	0.734	0.878	0.835
`v_9MHDmAMxO5I_c006`	0.727	0.910	0.791
`v_G-vNjfx1GGc_c600`	0.723	0.952	0.869
`v_G-vNjfx1GGc_c601`	0.649	0.878	0.724
`v_9MHDmAMxO5I_c004`	0.633	0.782	0.704

Benchmark — test split (performance only)

Measured on the full SportsMOT test split (150 sequences, 94,835 frames), staged as two mutually exclusive halves to fit Kaggle's kernel-output cap. The test split has no public ground truth (withheld for the SportsMOT challenge server), so no accuracy metrics are possible here — see the val-split benchmark above for those.

metric (Tesla T4)	value
throughput (batched x8, fp16 autocast, 4-thread prefetch)	28.0 fps (GPU-timed) · 26.5 fps wall-clock
single-image latency (batch 1, fp16, 300-frame probe)	mean 74.4 ms · p50 62.5 · p90 68.1 · p99 90.1
detections per frame (conf ≥ 0.5)	mean 10.2 · p50 10 · max 23
mean confidence of detections	0.920
model load time	5.2 s

Environment & method — the assumptions these numbers were measured under:


GPU	Tesla T4 (sm_75, 15.6 GB)
Software	torch 2.10.0+cu128 · CUDA 12.8 · transformers 5.0.0 · Python 3.12.13
CPU	4 vCPUs (image decode / prefetch)
Batching & precision	8 images per forward, fp16 autocast (fp32 weights)
Parallelism	4-thread image prefetch; part-2 tar extracted in a background thread during part-1 compute
Timing scope	CUDA-synchronized: preprocess + forward + postprocess per batch
Latency probe	separate batch-1 pass over the first 300 frames
Inputs	frame stride 1, confidence ≥ 0.5

Pipeline & reproducibility

This model was trained, evaluated, and benchmarked end-to-end on Kaggle's free tier, orchestrated from the HuggingFace workspace repository (staging → smoketest → run for each stage). The benchmark sections of this card are written by the evaluation/testing kernels themselves and update in place on re-runs.

Downloads last month: 142

Safetensors

Model size

43.7M params

Tensor type

F32

Model tree for smallTech/rtdetr-sportsmot

Base model

PekingU/rtdetr_v2_r50vd

Finetuned

(80)

this model

Dataset used to train smallTech/rtdetr-sportsmot

Space using smallTech/rtdetr-sportsmot 1

Evaluation results

Lekim89/sportsmot
Val Map View evaluation results source

mAP@[.5:.95]; full val split, unseen by training; torchmetrics COCO, conf 0.01
0.79 ^*
Val Map50 View evaluation results source

mAP@50; full val split, unseen by training
0.94 ^*
Val Map75 View evaluation results source

mAP@75; full val split, unseen by training
0.88 ^*

mAP@[.5:.95] on SportsMOT (val split, unseen by training)
validation set Kaggle evaluation kernel (benchmarks.json)

0.793
mAP@50 on SportsMOT (val split, unseen by training)
validation set Kaggle evaluation kernel (benchmarks.json)

0.938
mAP@75 on SportsMOT (val split, unseen by training)
validation set Kaggle evaluation kernel (benchmarks.json)

0.882