I will testing model using fasterrcnn_resnet50_fpn_2 and I found an error in my program like the following:

Building from model name arguments...
Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
100% 160M/160M [00:03<00:00, 52.4MB/s]
Frame: 1, Forward pass FPS: 0.820, Forward pass time: 1.220 seconds, Forward pass + annotation time: 1.226 seconds
qt.qpa.xcb: could not connect to display
qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "/usr/local/lib/python3.10/dist-packages/cv2/qt/plugins" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

Available platform plugins are: xcb.

Hello, I have been having some trouble training with the smoke custom dataset and I keep receiving the error below.

Traceback (most recent call last):
File "train.py", line 10, in
from torch_utils.engine import train_one_epoch, evaluate
ImportError: No module named torch_utils.engine

The problem seems to have risen from the python version match, and I could not find exactly which python version is used for this whole code. The version in my enviroment is 3.10.4.

Thank you.

First, thanks for the great work.

When I run without distributed mode, it works fine: This means that it created "output/training/" folder and saved the training results there.
However, when I try to run in a distributed mode, it does not work in my case:

1. Using argument option --dist_url 'tcp://localhost:23456' or 'tcp://127.0.0.1:23456' => In this case, it does not even generate the output directory "output/training/"
2. Using default option --dist_url "env://", which means I did nothing but to use default config => In this case, it created an output directory correctly but training is not happening and there are some files like "events.out.tfevents.1682197067.pid.0", "opt.yaml" and "train.log". Although "opt.yaml" has information about training parameters, the size of "train.log" is 0, i.e. it contains nothing.
   The thing I noticed is that in the file "opt.yaml" the value of gpu is 5, while our workstation has 10 GPUs.
   What could be the problem and how could be solved?

Dear Sir,

Could you please generate the training loss and training accuracy in one figure?

Thank you

Is there a way to extract image features after the fasterrcnn model has been trained?

And actually how to use this last_model_state.pth, like what info can I get from it?

Thanks for this light weight faster rcnn implementation!
However, I have been using Detectron2, and it is able to train on images without objects.
I think it would be good to use them as negative examples.

I tried to run this program, but the program can't run or error. Please to give me using onnx_inference_image.py

I try program:

fasterrcnn_mobilenetv3_large_fpn

!onnx_inference_image.py --weights outputs/training/fasterrcnn_resnet50_fpn_2/best_model.pth --input ../test/60rpm_sedikit --no-labels

But Error:
/bin/bash: onnx_inference_image.py: command not found

When I use the fasterrcnn model with backbone resnet101, I always have CUDA out of memory in the "evaluate" after one epoch. in the evaluate itself it gives an error at output=model(images). However, only after a few loops. So there is a leek to the CUDA memory somewhere I think. Further, my batch size is already only 1 and my images are 600 x600 pixels. any recommendations?
(my CUDA memory is 8GB)

Thanks in advance.

Hi sovit, its been a long time. I finally released from my finals. Now that I would like to continue to study this model, so that it could output the validation loss graph (because my thesis need it to check if the model overfit or underfit when applying it to an unseen dataset).

So, as last time, I had found a website: How can I determine validation loss for faster RCNN (PyTorch)?

And you have said it might required to break the model to several different module. So can you provide some guidance like how to break the model or how to implement the method used in the website?

What is your goto Hardware setup for training?
Is it Windows desktop with NVidia GPU or you usually run it on AWS/Azure?

Dear sovit-123,

First of all, thank you for your sharing your source code.
This is not an issue, I am just curious about how the pipeline feature show in your code?
I meant that which different point between the "training-pipeline" and "normal training" fasterrcnn-pytorch ?

Thank you in advance!

As the title stated, when training with custom dataset, the train and validation samples got double in the command promt, which I think affect the training speed and the accuracy of the training process. My dataset consists of 361 train samples and 42 validation samples.

Here is my train promt:
python train.py --data data_configs/traffic.yaml --epochs 50 --model fasterrcnn_resnet50_fpn --name trafficSign_detection_no_bg --batch 12

Here my dataconfig files:
TRAIN_DIR_IMAGES: data/traffic-sign/train/images
TRAIN_DIR_LABELS: data/traffic-sign/train/annotations
VALID_DIR_IMAGES: data/traffic-sign/valid/images
VALID_DIR_LABELS: data/traffic-sign/valid/annotations

CLASSES: [
'cam_di_nguoc_chieu',
'cam_oto',
'cam_oto_re_phai',
'cam_mo_to',
'cam_oto_va_moto',
'cam_ng_di_bo',
'cam_re_trai',
'cam_re_phai',
'cam_quay_dau_trai',
'max_spd_40',
'max_spd_50',
'max_spd_60',
'max_spd_80',
'cam_dung_do',
'cam_do',
'duong_giao_nhau',
'giao_nhau_vs_ko_uu_tien',
'giao_nhau_vs_ko_uu_tien_trai',
'giao_nhau_vs_uu_tien',
'dg_co_ng_di_bo_cat_ngang',
'tre_em_qua_duong',
'cong_truong',
'day_cap',
'slow',
'huong_phai_di',
'danh_cho_ng_di_bo',
'dg_mot_chieu',
'dg_cho_oto',
# 'background',
]
NC: 28
SAVE_VALID_PREDICTION_IMAGES: True`

the training process take a very long time of 2hour plus on RTX 3060, and the precision, the mAP is very low, around 0.23 for 50 epochs with batch_size of 12.

If I have images and txt file how to I train with your coda ??

Function 1 is to output validation loss plot using tensorboard
Function 2 is make something like results.csv to shows all the mAP on every epoch, so that we can know at which epoch we get the best mAP.

Example for result.csv is as figure below. This results.csv is from yolov5 one.

Hi, I am working in Kaggle with custom dataset. I got RuntimeError and I have no idea how to solve it. Can you help me?

RuntimeError: CUDA out of memory. Tried to allocate 2.44 GiB (GPU 0; 15.90 GiB total capacity; 9.95 GiB already allocated; 1.70 GiB free; 13.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

Traceback (most recent call last):
File "/content/fastercnn-pytorch-training-pipeline/train.py", line 542, in
main(args)
File "/content/fastercnn-pytorch-training-pipeline/train.py", line 494, in main
wandb_log(
File "/content/fastercnn-pytorch-training-pipeline/utils/logging.py", line 185, in wandb_log
if len(val_pred_image) == 1:
TypeError: object of type 'NoneType' has no len()
wandb: Waiting for W&B process to finish... (failed 1).

this is what I keep getting, I ran it yesterday and it worked today it isn't

Hi, I was using the same dataset which split into train/valid/test. When I use train.py, it has no problem at all, but when I execute validate.py it comes out this error. You have any ideas about it?

Hello! Does anyone have any idea how I can force feed the model images with potentially tricky objects so that it learns to avoid these biases? I was thinking of adding some background images in the training process (hand picked) with NO LABELS.

My question basically is: will they be tagged as background, and if so, will they end up in the loss function?

..
Drop files to upload them to session storage
Disk
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#koneksi kegoogle drive-
from google.colab import drive
drive.mount('/content/drive/')
Mounted at /content/drive/
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%cd /content/drive/MyDrive/cangkehdetection
/content/drive/MyDrive/cangkehdetection
[ ]

Download data

!git clone https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline.git
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%cd fasterrcnn-pytorch-training-pipeline
/content/drive/MyDrive/cangkehdetection/fasterrcnn-pytorch-training-pipeline
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Install the Requirements library for image detection-

!pip install -r requirements.txt
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Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision==0.13.0->-r requirements.txt (line 14)) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision==0.13.0->-r requirements.txt (line 14)) (2022.12.7)
Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision==0.13.0->-r requirements.txt (line 14)) (2.0.12)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision==0.13.0->-r requirements.txt (line 14)) (1.26.15)
Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.9/dist-packages (from werkzeug>=1.0.1->tensorboard->-r requirements.txt (line 22)) (2.1.2)
Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.9/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 4)) (6.2)
Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.9/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 4)) (0.2.0)
Requirement already satisfied: jupyter-client in /usr/local/lib/python3.9/dist-packages (from ipykernel->jupyter->-r requirements.txt (line 4)) (6.1.12)
Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.9/dist-packages (from ipywidgets->jupyter->-r requirements.txt (line 4)) (3.6.4)
Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from ipywidgets->jupyter->-r requirements.txt (line 4)) (3.0.7)
Requirement already satisfied: jupyter-core>=4.7 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (5.3.0)
Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (0.8.4)
Requirement already satisfied: jinja2>=3.0 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (3.1.2)
Requirement already satisfied: bleach in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (6.0.0)
Requirement already satisfied: lxml in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (4.9.2)
Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (4.11.2)
Requirement already satisfied: defusedxml in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (0.7.1)
Requirement already satisfied: nbformat>=5.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (5.8.0)
Requirement already satisfied: tinycss2 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (1.2.1)
Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (1.5.0)
Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (0.2.2)
Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (0.4)
Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.9/dist-packages (from nbconvert->jupyter->-r requirements.txt (line 4)) (0.7.3)
Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.9/dist-packages (from notebook->jupyter->-r requirements.txt (line 4)) (23.2.1)
Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.9/dist-packages (from notebook->jupyter->-r requirements.txt (line 4)) (0.17.1)
Requirement already satisfied: nest-asyncio>=1.5 in /usr/local/lib/python3.9/dist-packages (from notebook->jupyter->-r requirements.txt (line 4)) (1.5.6)
Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.9/dist-packages (from notebook->jupyter->-r requirements.txt (line 4)) (1.8.0)
Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.9/dist-packages (from notebook->jupyter->-r requirements.txt (line 4)) (21.3.0)
Requirement already satisfied: prometheus-client in /usr/local/lib/python3.9/dist-packages (from notebook->jupyter->-r requirements.txt (line 4)) (0.16.0)
Collecting qtpy>=2.0.1
Downloading QtPy-2.3.1-py3-none-any.whl (84 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 84.9/84.9 KB 10.8 MB/s eta 0:00:00
Collecting smmap<6,>=3.0.1
Downloading smmap-5.0.0-py3-none-any.whl (24 kB)
Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.9/dist-packages (from jupyter-core>=4.7->nbconvert->jupyter->-r requirements.txt (line 4)) (3.2.0)
Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.9/dist-packages (from nbformat>=5.1->nbconvert->jupyter->-r requirements.txt (line 4)) (4.3.3)
Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.9/dist-packages (from nbformat>=5.1->nbconvert->jupyter->-r requirements.txt (line 4)) (2.16.3)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.9/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->-r requirements.txt (line 22)) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard->-r requirements.txt (line 22)) (3.2.2)
Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.9/dist-packages (from argon2-cffi->notebook->jupyter->-r requirements.txt (line 4)) (21.2.0)
Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->nbconvert->jupyter->-r requirements.txt (line 4)) (2.4)
Requirement already satisfied: webencodings in /usr/local/lib/python3.9/dist-packages (from bleach->nbconvert->jupyter->-r requirements.txt (line 4)) (0.5.1)
Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->-r requirements.txt (line 4)) (22.2.0)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter->-r requirements.txt (line 4)) (0.19.3)
Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook->jupyter->-r requirements.txt (line 4)) (1.15.1)
Requirement already satisfied: pycparser in /usr/local/lib/python3.9/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook->jupyter->-r requirements.txt (line 4)) (2.21)
Building wheels for collected packages: pathtools
Building wheel for pathtools (setup.py) ... done
Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8807 sha256=ce341c5849e5e6a3c8e4b833e3b11c039a73e68dc3da1279e37853635e92362b
Stored in directory: /root/.cache/pip/wheels/b7/0a/67/ada2a22079218c75a88361c0782855cc72aebc4d18d0289d05
Successfully built pathtools
Installing collected packages: pathtools, torchinfo, torch, smmap, setuptools, setproctitle, sentry-sdk, qtpy, protobuf, jedi, docker-pycreds, torchvision, torchmetrics, gitdb, GitPython, wandb, qtconsole, jupyter
Attempting uninstall: torch
Found existing installation: torch 2.0.0+cu118
Uninstalling torch-2.0.0+cu118:
Successfully uninstalled torch-2.0.0+cu118
Attempting uninstall: setuptools
Found existing installation: setuptools 67.6.1
Uninstalling setuptools-67.6.1:
Successfully uninstalled setuptools-67.6.1
Attempting uninstall: protobuf
Found existing installation: protobuf 3.20.3
Uninstalling protobuf-3.20.3:
Successfully uninstalled protobuf-3.20.3
Attempting uninstall: torchvision
Found existing installation: torchvision 0.15.1+cu118
Uninstalling torchvision-0.15.1+cu118:
Successfully uninstalled torchvision-0.15.1+cu118
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchtext 0.15.1 requires torch==2.0.0, but you have torch 1.12.0 which is incompatible.
torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.12.0 which is incompatible.
torchaudio 2.0.1+cu118 requires torch==2.0.0, but you have torch 1.12.0 which is incompatible.
tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.
googleapis-common-protos 1.59.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-cloud-translate 3.8.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-cloud-language 2.6.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-cloud-firestore 2.7.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-cloud-datastore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-cloud-bigquery 3.4.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-cloud-bigquery-storage 2.19.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.
cvxpy 1.3.1 requires setuptools>65.5.1, but you have setuptools 59.5.0 which is incompatible.
arviz 0.15.1 requires setuptools>=60.0.0, but you have setuptools 59.5.0 which is incompatible.
Successfully installed GitPython-3.1.31 docker-pycreds-0.4.0 gitdb-4.0.10 jedi-0.18.2 jupyter-1.0.0 pathtools-0.1.2 protobuf-3.20.1 qtconsole-5.4.2 qtpy-2.3.1 sentry-sdk-1.19.1 setproctitle-1.3.2 setuptools-59.5.0 smmap-5.0.0 torch-1.12.0 torchinfo-1.7.2 torchmetrics-0.11.4 torchvision-0.13.0 wandb-0.14.2
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!pip install torch==2.0.0
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting torch==2.0.0
Downloading torch-2.0.0-cp39-cp39-manylinux1_x86_64.whl (619.9 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 619.9/619.9 MB 2.4 MB/s eta 0:00:00
Collecting nvidia-cuda-nvrtc-cu11==11.7.99
Downloading nvidia_cuda_nvrtc_cu11-11.7.99-2-py3-none-manylinux1_x86_64.whl (21.0 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.0/21.0 MB 63.4 MB/s eta 0:00:00
Collecting nvidia-cuda-runtime-cu11==11.7.99
Downloading nvidia_cuda_runtime_cu11-11.7.99-py3-none-manylinux1_x86_64.whl (849 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 849.3/849.3 KB 56.1 MB/s eta 0:00:00
Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch==2.0.0) (3.10.7)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch==2.0.0) (3.1.2)
Collecting nvidia-cusparse-cu11==11.7.4.91
Downloading nvidia_cusparse_cu11-11.7.4.91-py3-none-manylinux1_x86_64.whl (173.2 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 173.2/173.2 MB 7.3 MB/s eta 0:00:00
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch==2.0.0) (4.5.0)
Collecting nvidia-nvtx-cu11==11.7.91
Downloading nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl (98 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 98.6/98.6 KB 13.6 MB/s eta 0:00:00
Collecting nvidia-cufft-cu11==10.9.0.58
Downloading nvidia_cufft_cu11-10.9.0.58-py3-none-manylinux1_x86_64.whl (168.4 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 168.4/168.4 MB 7.4 MB/s eta 0:00:00
Collecting nvidia-cuda-cupti-cu11==11.7.101
Downloading nvidia_cuda_cupti_cu11-11.7.101-py3-none-manylinux1_x86_64.whl (11.8 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 11.8/11.8 MB 84.9 MB/s eta 0:00:00
Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch==2.0.0) (1.11.1)
Collecting nvidia-curand-cu11==10.2.10.91
Downloading nvidia_curand_cu11-10.2.10.91-py3-none-manylinux1_x86_64.whl (54.6 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 54.6/54.6 MB 12.4 MB/s eta 0:00:00
Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch==2.0.0) (3.0)
Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch==2.0.0) (2.0.0)
Collecting nvidia-cublas-cu11==11.10.3.66
Downloading nvidia_cublas_cu11-11.10.3.66-py3-none-manylinux1_x86_64.whl (317.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 317.1/317.1 MB 4.0 MB/s eta 0:00:00
Collecting nvidia-cudnn-cu11==8.5.0.96
Downloading nvidia_cudnn_cu11-8.5.0.96-2-py3-none-manylinux1_x86_64.whl (557.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 557.1/557.1 MB 2.7 MB/s eta 0:00:00
Collecting nvidia-nccl-cu11==2.14.3
Downloading nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl (177.1 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 177.1/177.1 MB 6.2 MB/s eta 0:00:00
Collecting nvidia-cusolver-cu11==11.4.0.1
Downloading nvidia_cusolver_cu11-11.4.0.1-2-py3-none-manylinux1_x86_64.whl (102.6 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 102.6/102.6 MB 9.9 MB/s eta 0:00:00
Requirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch==2.0.0) (59.5.0)
Requirement already satisfied: wheel in /usr/local/lib/python3.9/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch==2.0.0) (0.40.0)
Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch==2.0.0) (3.25.2)
Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch==2.0.0) (16.0.0)
Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch==2.0.0) (2.1.2)
Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch==2.0.0) (1.3.0)
Installing collected packages: nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, nvidia-cusolver-cu11, nvidia-cudnn-cu11, torch
Attempting uninstall: torch
Found existing installation: torch 1.12.0
Uninstalling torch-1.12.0:
Successfully uninstalled torch-1.12.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchvision 0.13.0 requires torch==1.12.0, but you have torch 2.0.0 which is incompatible.
Successfully installed nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 torch-2.0.0
[7]
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!pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI
Cloning https://github.com/gautamchitnis/cocoapi.git (to revision cocodataset-master) to /tmp/pip-req-build-ojbw02d9
Running command git clone --filter=blob:none --quiet https://github.com/gautamchitnis/cocoapi.git /tmp/pip-req-build-ojbw02d9
Running command git checkout -b cocodataset-master --track origin/cocodataset-master
Switched to a new branch 'cocodataset-master'
Branch 'cocodataset-master' set up to track remote branch 'cocodataset-master' from 'origin'.
Resolved https://github.com/gautamchitnis/cocoapi.git to commit 20291f19c46a8d11935862bc9e449a1b72ec25ed
Preparing metadata (setup.py) ... done
Building wheels for collected packages: pycocotools
Building wheel for pycocotools (setup.py) ... done
Created wheel for pycocotools: filename=pycocotools-2.0-cp39-cp39-linux_x86_64.whl size=398095 sha256=94723d5ce3fca91072d2832e8579000019a077fbabd5edf0c6c4db10f27932f7
Stored in directory: /tmp/pip-ephem-wheel-cache-n06x8qwe/wheels/a6/5f/ec/1eaf8c69abab5724baee819736e6d30adad774deb60736413b
Successfully built pycocotools
Installing collected packages: pycocotools
Attempting uninstall: pycocotools
Found existing installation: pycocotools 2.0.6
Uninstalling pycocotools-2.0.6:
Successfully uninstalled pycocotools-2.0.6
Successfully installed pycocotools-2.0
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#membuat file YAML (konfigurasi datatrain)-
%%writefile data_configs/clove_2023.yaml

Direktori gambar dan label harus relatif terhadap train.py ()

TRAIN_DIR_IMAGES: 'custom_data/train'
TRAIN_DIR_LABELS: 'custom_data/train'

VALID_DIR_IMAGES: 'custom_data/valid'
VALID_DIR_LABELS: 'custom_data/valid'

Masukkan nama classs

CLASSES: [
'background',
'clove', 'rubbish'
]

Jumlah class yang dijadikan object (object classes + 1 for background class in Faster RCNN).

NC: 3

Pertanyaan apakan akan menyimpan prediksi set validasi saat pelatihan jika ya ketik "True" dan jika tidak ketik "False"

SAVE_VALID_PREDICTION_IMAGES: True
Writing data_configs/clove_2023.yaml
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50s

install library

!pip install vision_transformers
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting vision_transformers
Downloading vision_transformers-0.1.1.0.tar.gz (38 kB)
Preparing metadata (setup.py) ... done
Requirement already satisfied: torch>=1.10 in /usr/local/lib/python3.9/dist-packages (from vision_transformers) (2.0.0)
Requirement already satisfied: torchvision in /usr/local/lib/python3.9/dist-packages (from vision_transformers) (0.13.0)
Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.7.99)
Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.7.4.91)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (4.5.0)
Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (2.14.3)
Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.10.3.66)
Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.7.91)
Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (10.2.10.91)
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Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.7.101)
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Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (8.5.0.96)
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Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (3.10.7)
Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.4.0.1)
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Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.9/dist-packages (from torch>=1.10->vision_transformers) (11.7.99)
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Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from torchvision->vision_transformers) (2.27.1)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.9/dist-packages (from torchvision->vision_transformers) (8.4.0)
Collecting torch>=1.10
Using cached torch-1.12.0-cp39-cp39-manylinux1_x86_64.whl (776.3 MB)
Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torchvision->vision_transformers) (1.22.4)
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Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision->vision_transformers) (2022.12.7)
Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch>=1.10->vision_transformers) (1.3.0)
Building wheels for collected packages: vision_transformers
Building wheel for vision_transformers (setup.py) ... done
Created wheel for vision_transformers: filename=vision_transformers-0.1.1.0-py3-none-any.whl size=48431 sha256=64be1b21506d11b9bba0bf2d026e79cfff9fc4133238bf701a865cc4e191d9bb
Stored in directory: /root/.cache/pip/wheels/1d/86/13/31f5fe3a4f2bc3dd24af79c12d5f732256e5047cb70d7485ef
Successfully built vision_transformers
Installing collected packages: torch, vision_transformers
Attempting uninstall: torch
Found existing installation: torch 2.0.0
Uninstalling torch-2.0.0:
Successfully uninstalled torch-2.0.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torchtext 0.15.1 requires torch==2.0.0, but you have torch 1.12.0 which is incompatible.
torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.12.0 which is incompatible.
torchaudio 2.0.1+cu118 requires torch==2.0.0, but you have torch 1.12.0 which is incompatible.
Successfully installed torch-1.12.0 vision_transformers-0.1.1.0
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1m

Old

!python train.py --model fasterrcnn_mobilenetv3_large_fpn --data data_configs/clove_2023.yaml --epochs 40 --no-mosaic --name training_old --seed 42
Not using distributed mode
wandb: Currently logged in as: auliadarnilasarii97 (mandev). Use wandb login --relogin to force relogin
wandb: Tracking run with wandb version 0.14.2
wandb: Run data is saved locally in /content/drive/MyDrive/cangkehdetection/fasterrcnn-pytorch-training-pipeline/wandb/run-20230409_064846-ws99a2ql
wandb: Run wandb offline to turn off syncing.
wandb: Syncing run training_old
wandb: ⭐️ View project at https://wandb.ai/mandev/fasterrcnn-pytorch-training-pipeline
wandb: 🚀 View run at https://wandb.ai/mandev/fasterrcnn-pytorch-training-pipeline/runs/ws99a2ql
device cuda
2023-04-09 06:48:47.993981: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-09 06:48:49.286339: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Creating data loaders
/usr/local/lib/python3.9/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(_create_warning_msg(
Number of training samples: 1470
Number of validation samples: 179

Building model from scratch...
/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or None for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing weights=FasterRCNN_MobileNet_V3_Large_FPN_Weights.COCO_V1. You can also use weights=FasterRCNN_MobileNet_V3_Large_FPN_Weights.DEFAULT to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth" to /root/.cache/torch/hub/checkpoints/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth
100% 74.2M/74.2M [00:00<00:00, 88.0MB/s]
Traceback (most recent call last):
File "/content/drive/MyDrive/cangkehdetection/fasterrcnn-pytorch-training-pipeline/train.py", line 539, in
main(args)
File "/content/drive/MyDrive/cangkehdetection/fasterrcnn-pytorch-training-pipeline/train.py", line 331, in main
model = model.to(DEVICE)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 927, in to
return self._apply(convert)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 579, in _apply
module._apply(fn)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 579, in _apply
module._apply(fn)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 579, in _apply
module._apply(fn)
[Previous line repeated 1 more time]
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 602, in _apply
param_applied = fn(param)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 925, in convert
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
File "/usr/local/lib/python3.9/dist-packages/torch/cuda/init.py", line 217, in _lazy_init
torch._C._cuda_init()
RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
wandb: 🚀 View run training_old at: https://wandb.ai/mandev/fasterrcnn-pytorch-training-pipeline/runs/ws99a2ql
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20230409_064846-ws99a2ql/logs

The train.py file accepts --model and --weights arguments and when I use the --model argument, training is from scratch. And when I use --weights and set the path to a pth file, I am getting this error:

Loading pretrained weights... Traceback (most recent call last): File "E:\fastercnn-pytorch-training-pipeline\train.py", line 547, in <module> main(args) File "E:\fastercnn-pytorch-training-pipeline\train.py", line 293, in main keys = list(checkpoint['model_state_dict'].keys()) KeyError: 'model_state_dict'

I got this error and I tried to solve it by install vision-transformer, but still show me same error
pip install vision-transformer-pytorch

I often use this repo as a point of reference.

boxes = [] 
boxes = torch.as_tensor(boxes, dtype=torch.float32)

I don't believe the above would create the required shape to be provided to Faster RCNN in the case of an empty frame. See https://stackoverflow.com/questions/66063046/how-to-train-faster-rcnn-on-dataset-including-negative-data-in-pytorch .

Does your dataset / model cope with empty frames in some other way? Just trying to find what's considered sensible practice.

pytorch/vision#1598

Wanted to train smoke and my setup went fine.. I tried the following and I am getting the following...
Is there a way to skip creating W&B account?

python train.py --config data_configs/smoke.yaml --epochs 100 --model fasterrcnn_resnet50_fpn --project-name smoke_training --batch-size 16
Not using distributed mode
wandb: (1) Create a W&B account
wandb: (2) Use an existing W&B account
wandb: (3) Don't visualize my results
wandb: Enter your choice:

I have been trying to implement my own custom dataset and it works but it seems like the data is red from files every epoch, is there no way of caching the data? I have mostly worked in Tensorflow and it can get a tremendous improvement in speed by prefetching and caching the data.

Is there any instruction to follow for creating a distributed execution? Preferably for execution on a single machine with multiple GPUs.
I can se that it should be possible but I don't understand how to do it.

ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /home/noor/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/pandas/_libs/window/aggregations.cpython-39-x86_64-linux-gnu.so)

My bounding box is in "Pascal VOC" format. While training a model, I got this error:

ValueError: x_max is less than or equal to x_min for bbox (tensor(0.3252), tensor(0.9222), tensor(0.3252), tensor(0.9308),tensor(2)).

Love this repo.

I tend to default to using the resnet50_v2 backbone (best performer in torchvision object detectionmetrics). Curious if your experiments suggest that this is a good choice or not?

And if you're not sure I'd be tempted to perform some training runs and write them up.

I got a something error that about index over...

see the error line, I think the reason of that is too much big parameter..

is it right?

why I get a error and how can I solved during training data

File "train.py", line 200, in <module> colors=COLORS File "/home/ubuntu/anaconda3/envs/pytorch1.7.1_p37/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/home/ubuntu/lecttue-diagonosis/YangDongJae/fastercnn-pytorch-training-pipeline/torch_utils/engine.py", line 138, in evaluate images, outputs, counter, out_dir, classes, colors File "/home/ubuntu/lecttue-diagonosis/YangDongJae/fastercnn-pytorch-training-pipeline/utils/general.py", line 218, in save_validation_results pred_classes = [classes[i] for i in labels.cpu().numpy()] File "/home/ubuntu/lecttue-diagonosis/YangDongJae/fastercnn-pytorch-training-pipeline/utils/general.py", line 218, in <listcomp> pred_classes = [classes[i] for i in labels.cpu().numpy()] IndexError: list index out of range

give me reply

@sovit-123
i run training on my pc and power was off i need to resume training.

and also sorry if i do training for like 20 epoic and training finished but the result i got is not good how to resume from last weight ,like start from epoic 21 .

I want to disable the image augmentation, could you please show me how!

Thank you!

Hi,

I would like to use your repo to run fasterRCNN on my custom dataset. I would like to validate if detections are TP,FP, and FN and use it to compute TruePositiveRate, Precision, Recall ,and F-1. Could you please guide me how I should this for the inference mode?
Do you think I can use the following section in eval.py to compute these metrics:
for i in range(len(images)): true_dict = dict() preds_dict = dict() true_dict['boxes'] = targets[i]['boxes'].detach().cpu() true_dict['labels'] = targets[i]['labels'].detach().cpu() preds_dict['boxes'] = outputs[i]['boxes'].detach().cpu() preds_dict['scores'] = outputs[i]['scores'].detach().cpu() preds_dict['labels'] = outputs[i]['labels'].detach().cpu() preds.append(preds_dict) target.append(true_dict)
I only see the average precision.

In the eval.py you use AverageMeanPrecision from torchmetrics, in the train.py for evaluation you use the evaluation using pycocotools. When I use them both, I obtain different values for AP. Which one do you recommend to use and why?

I want to train the model, but I got this error even the batch size = 1

Is it possible to add to the error message the file that causes the error?

I am trying to train the model on the RICO dataset from kaggle ( you can find it here https://www.kaggle.com/datasets/onurgunes1993/rico-dataset ) and using colab. The dimensions of each image mentioned in the labels ( 1440 * 2560 ) do not match the actual dimensions when opening the images using openCV ( 1080 * 1920 ).
After converting the labels to Pascal voc format, I have tried rescaling the labels, resizing the images, normalizing the dimensions and deleting the images and labels whose normalized dimensions are not in the range [0,1], but I am still getting the following error

Epoch: [0]  [  0/174]  eta: 0:11:26  lr: 0.000007  loss: 3.9243 (3.9243)  loss_classifier: 0.7015 (0.7015)  loss_box_reg: 0.3106 (0.3106)  loss_objectness: 2.6861 (2.6861)  loss_rpn_box_reg: 0.2260 (0.2260)  time: 3.9457  data: 2.4448  max mem: 1315
Traceback (most recent call last):
  File "/content/fasterrcnn-pytorch-training-pipeline/train.py", line 561, in <module>
    main(args)
  File "/content/fasterrcnn-pytorch-training-pipeline/train.py", line 401, in main
    batch_loss_rpn_list = train_one_epoch(
  File "/content/fasterrcnn-pytorch-training-pipeline/torch_utils/engine.py", line 45, in train_one_epoch
    for images, targets in metric_logger.log_every(data_loader, print_freq, header):
  File "/content/fasterrcnn-pytorch-training-pipeline/torch_utils/utils.py", line 173, in log_every
    for obj in iterable:
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 633, in __next__
    data = self._next_data()
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1345, in _next_data
    return self._process_data(data)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 1371, in _process_data
    data.reraise()
  File "/usr/local/lib/python3.10/dist-packages/torch/_utils.py", line 644, in reraise
    raise exception
ValueError: Caught ValueError in DataLoader worker process 2.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py", line 51, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/content/fasterrcnn-pytorch-training-pipeline/datasets.py", line 327, in __getitem__
    sample = self.transforms(image=image_resized,
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/composition.py", line 202, in __call__
    p.preprocess(data)
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/utils.py", line 83, in preprocess
    data[data_name] = self.check_and_convert(data[data_name], rows, cols, direction="to")
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/utils.py", line 91, in check_and_convert
    return self.convert_to_albumentations(data, rows, cols)
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/bbox_utils.py", line 126, in convert_to_albumentations
    return convert_bboxes_to_albumentations(data, self.params.format, rows, cols, check_validity=True)
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/bbox_utils.py", line 394, in convert_bboxes_to_albumentations
    return [convert_bbox_to_albumentations(bbox, source_format, rows, cols, check_validity) for bbox in bboxes]
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/bbox_utils.py", line 394, in <listcomp>
    return [convert_bbox_to_albumentations(bbox, source_format, rows, cols, check_validity) for bbox in bboxes]
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/bbox_utils.py", line 337, in convert_bbox_to_albumentations
    check_bbox(bbox)
  File "/usr/local/lib/python3.10/dist-packages/albumentations/core/bbox_utils.py", line 421, in check_bbox
    raise ValueError(
ValueError: Expected x_min for bbox (tensor(-0.0028), tensor(0.0527), tensor(0.0021), tensor(0.0891), tensor(1)) to be in the range [0.0, 1.0], got -0.0027777778450399637.

One more extra question is that does this repo require any specific dataset format? Like in the example is voc format?

Is there a way to get the precision/recall for a specific threshold ? Or a way to find the best threshold for my model

when l try to run eval.py; i get the following error

Traceback (most recent call last):
File "/home/noor/fasterrcnn-pytorch-training-pipeline/eval.py", line 121, in
model.load_state_dict(checkpoint['model_state_dict'])
File "/home/noor/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1604, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for FasterRCNN:
Missing key(s) in state_dict: "backbone.fpn.inner_blocks.0.1.weight", "backbone.fpn.inner_blocks.0.1.bias", "backbone.fpn.inner_blocks.0.1.running_mean", "backbone.fpn.inner_blocks.0.1.running_var", "backbone.fpn.inner_blocks.1.1.weight", "backbone.fpn.inner_blocks.1.1.bias", "backbone.fpn.inner_blocks.1.1.running_mean", "backbone.fpn.inner_blocks.1.1.running_var", "backbone.fpn.inner_blocks.2.1.weight", "backbone.fpn.inner_blocks.2.1.bias", "backbone.fpn.inner_blocks.2.1.running_mean", "backbone.fpn.inner_blocks.2.1.running_var", "backbone.fpn.inner_blocks.3.1.weight", "backbone.fpn.inner_blocks.3.1.bias", "backbone.fpn.inner_blocks.3.1.running_mean", "backbone.fpn.inner_blocks.3.1.running_var", "backbone.fpn.layer_blocks.0.1.weight", "backbone.fpn.layer_blocks.0.1.bias", "backbone.fpn.layer_blocks.0.1.running_mean", "backbone.fpn.layer_blocks.0.1.running_var", "backbone.fpn.layer_blocks.1.1.weight", "backbone.fpn.layer_blocks.1.1.bias", "backbone.fpn.layer_blocks.1.1.running_mean", "backbone.fpn.layer_blocks.1.1.running_var", "backbone.fpn.layer_blocks.2.1.weight", "backbone.fpn.layer_blocks.2.1.bias", "backbone.fpn.layer_blocks.2.1.running_mean", "backbone.fpn.layer_blocks.2.1.running_var", "backbone.fpn.layer_blocks.3.1.weight", "backbone.fpn.layer_blocks.3.1.bias", "backbone.fpn.layer_blocks.3.1.running_mean", "backbone.fpn.layer_blocks.3.1.running_var", "rpn.head.conv.1.0.weight", "rpn.head.conv.1.0.bias", "roi_heads.box_head.0.0.weight", "roi_heads.box_head.0.1.weight", "roi_heads.box_head.0.1.bias", "roi_heads.box_head.0.1.running_mean", "roi_heads.box_head.0.1.running_var", "roi_heads.box_head.1.0.weight", "roi_heads.box_head.1.1.weight", "roi_heads.box_head.1.1.bias", "roi_heads.box_head.1.1.running_mean", "roi_heads.box_head.1.1.running_var", "roi_heads.box_head.2.0.weight", "roi_heads.box_head.2.1.weight", "roi_heads.box_head.2.1.bias", "roi_heads.box_head.2.1.running_mean", "roi_heads.box_head.2.1.running_var", "roi_heads.box_head.3.0.weight", "roi_heads.box_head.3.1.weight", "roi_heads.box_head.3.1.bias", "roi_heads.box_head.3.1.running_mean", "roi_heads.box_head.3.1.running_var", "roi_heads.box_head.5.weight", "roi_heads.box_head.5.bias".
Unexpected key(s) in state_dict: "backbone.fpn.inner_blocks.0.0.bias", "backbone.fpn.inner_blocks.1.0.bias", "backbone.fpn.inner_blocks.2.0.bias", "backbone.fpn.inner_blocks.3.0.bias", "backbone.fpn.layer_blocks.0.0.bias", "backbone.fpn.layer_blocks.1.0.bias", "backbone.fpn.layer_blocks.2.0.bias", "backbone.fpn.layer_blocks.3.0.bias", "roi_heads.box_head.fc6.weight", "roi_heads.box_head.fc6.bias", "roi_heads.box_head.fc7.weight", "roi_heads.box_head.fc7.bias".

I am using roboflow to manage the dataset. FYI roboflow can export a dataset to different format.

usage: train.py
[-h]
[-m MODEL]
[--data DATA]
[-d DEVICE]
[-e EPOCHS]
[-j WORKERS]
[-b BATCH]
[--lr LR]
[-ims IMGSZ]
[-n NAME]
[-vt]
[-nm]
[-uta]
[-ca]
[-w WEIGHTS]
[-r]
[-st]
[--world-size WORLD_SIZE]
[--dist-url DIST_URL]
[-dw]
[--seed SEED]
train.py: error: unrecognized arguments: --config data_configs/wheat_2020.yaml --project-name fasterrcnn_mobilenetv3_large_fpn_noaug_40e

I am trying to run the code on my custom dataset but I got this error.

Building model from scratch...
Traceback (most recent call last):
  File "train.py", line 491, in <module>
    main(args)
  File "train.py", line 248, in main
    build_model = create_model[args['model']]
KeyError: 'fasterrcnn_resnet50'
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
wandb: Synced smoke_training: https://wandb.ai/samahwa/fastercnn-pytorch-training-pipeline/runs/ejy5jyw8
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20221128_113545-ejy5jyw8/logs

Traceback (most recent call last):
File "/home/noor/fasterrcnn-pytorch-training-pipeline/train.py", line 539, in
main(args)
File "/home/noor/fasterrcnn-pytorch-training-pipeline/train.py", line 251, in main
train_sampler = RandomSampler(train_dataset)
File "/home/noor/anaconda3/envs/tf-gpu/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 107, in init
raise ValueError("num_samples should be a positive integer "

1. I want to use resnet-101 model but I can't find it in the models folder. Could you please help me!?

2. How I can find the precision and recall for each object?

Thank you

Hi! I would love to know if there is a way or could be implemented a way of generating a confusion matrix while validation, among other metrics as F1-score.