TLT yolo_v4 int8 model does not detect anything

I’m going through the TLT yolo_v4 jupyter notebook and the exported int8 model does not detect anything.
the fp16 and fp32 have pretty good results.
when I run the evaluate command on the trt engine file I get this:

Start to calculate AP for each class

car AP 0.0
cyclist AP 0.0
pedestrian AP 0.0
mAP 0.0

thanks for your help

For default yolo_v4 jupyter notebook, I cannot reproduce the issue.
Can you double check? If possible, could you share your .ipynb file?

yolo_v4.ipynb (19.7 MB)

this is my .ipynb file

Could you please replace the -cal_image_dir as below and rerun section 10 of the notebook?

!tlt yolo_v4 export -m $USER_EXPERIMENT_DIR/experiment_dir_retrain/weights/yolov4_resnet18_epoch_$EPOCH.tlt
-o $USER_EXPERIMENT_DIR/export8/yolov4_resnet18_epoch_$EPOCH.etlt
-e $SPECS_DIR/yolo_v4_retrain_resnet18_kitti.txt
-k $KEY
–cal_image_dir $USER_EXPERIMENT_DIR/data/training/image_2
–data_type int8
–batch_size 16
–batches 10
–cal_cache_file $USER_EXPERIMENT_DIR/export8/cal.bin
–cal_data_file $USER_EXPERIMENT_DIR/export8/cal.tensorfile

It didn’t solve the problem

To narrow down, please try to export an etlt file against an unpruned tlt model.

It doesn’t work with the unpruned model either.

Actually I cannot reproduce the issue. More question, which dgpu did you use for training? Can it support INT8?

I work on a NVIDIA ec2 instance on AWS so i’m not so sure.
When I type “inxi -G” I get this output:
Graphics: Card-1: Device 1111
Card-2: NVIDIA Device 1eb8
Display Server: Moba/X 12.4 driver: nvidia Resolution: 4656x1080@0.00hz
OpenGL: renderer: N/A version: N/A

does it answer your question?

Could you run $ nvidia-smi ?

| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
| 0 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 24C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |

| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
| No running processes found |

So, T4 is running.
To narrow down, please try to run other network’s jupyter notebook, for example, detectnet_v2 and yolo_v3.

I tried to run the jupyter for yolo_v3 and I ran into the same problem

Thanks for the info. I need to check further at AWS instance.

Great. Thanks.