Issue with using YOLO-NAS with Ultralytics: TypeError: DetectionValidator.postprocess() got an unexpected keyword argument 'max_time_img'

[SamArrows]

🐛 Describe the bug

I want to use YOLO-NAS in my object detection project using images. As such, I just want to initially check how it performs on my test set to compare it to other YOLO models I've trained and tested. When I run code from Ultralytics simply for COCO though, I get an error. The code is as follows:

from ultralytics import NAS
model = NAS("yolo_nas_s.pt")
model.info()
results = model.val(data="coco8.yaml")

I am getting the following errors:

---

TypeError Traceback (most recent call last)
in <cell line: 10>()
8
9 # Validate the model on the COCO8 example dataset
---> 10 results = model.val(data="coco8.yaml")

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py in val(self, validator, **kwargs)
626
627 validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks)
--> 628 validator(model=self.model)
629 self.metrics = validator.metrics
630 return validator.metrics

/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
114 def decorate_context(*args, **kwargs):
115 with ctx_factory():
--> 116 return func(*args, **kwargs)
117
118 return decorate_context

/usr/local/lib/python3.10/dist-packages/ultralytics/engine/validator.py in call(self, trainer, model)
221 # Postprocess
222 with dt[3]:
--> 223 preds = self.postprocess(preds)
224
225 self.update_metrics(preds, batch)

/usr/local/lib/python3.10/dist-packages/ultralytics/models/nas/val.py in postprocess(self, preds_in)
37 boxes = ops.xyxy2xywh(preds_in[0][0]) # Convert bounding box format from xyxy to xywh
38 preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1) # Concatenate boxes with scores and permute
---> 39 return super().postprocess(
40 preds,
41 max_time_img=0.5,

TypeError: DetectionValidator.postprocess() got an unexpected keyword argument 'max_time_img'

As far as I am aware, my super-gradients and Ultralytics modules are the latest version. I've tried reverting to a few combinations too but that ends up yielding more complicated errors. I feel like it should be a straightforward fix or maybe just revert to a different version. For reference, my versions are:

• ultralytics: 8.3.96
• super-gradients: 3.7.1

If anyone knows an exact set-up for the versions which definitely runs, I would be more than happy to use it - I don't need the latest versions of everything, I just need to get something that runs. For reference, I am using Kaggle notebooks.

Versions

--2025-03-25 16:38:35-- https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 24440 (24K) [text/plain]
Saving to: ‘collect_env.py’

collect_env.py 100%[===================>] 23.87K --.-KB/s in 0.002s

2025-03-25 16:38:35 (10.2 MB/s) - ‘collect_env.py’ saved [24440/24440]

Collecting environment information...
PyTorch version: 2.5.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.6.56+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Tesla T4
GPU 1: Tesla T4

Nvidia driver version: 560.35.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 4
On-line CPU(s) list: 0-3
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 2
Socket(s): 1
Stepping: 3
BogoMIPS: 4000.33
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 64 KiB (2 instances)
L1i cache: 64 KiB (2 instances)
L2 cache: 2 MiB (2 instances)
L3 cache: 38.5 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.0
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.6.0.74
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-nccl-cu12==2.23.4
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvtx==0.2.10
[pip3] onnx==1.15.0
[pip3] onnxruntime==1.15.0
[pip3] onnxsim==0.4.36
[pip3] optree==0.13.1
[pip3] pynvjitlink-cu12==0.4.0
[pip3] pytorch-ignite==0.5.1
[pip3] pytorch-lightning==2.5.0.post0
[pip3] torch==2.5.1+cu121
[pip3] torchaudio==2.5.1+cu121
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==0.8.0
[pip3] torchsummary==1.5.1
[pip3] torchtune==0.5.0
[pip3] torchvision==0.20.1+cu121
[conda] Could not collect