Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline
PyTorch Faster R-CNN Object Detection on Custom Dataset
https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline
darknet-fasterrcnn deep-learning efficientnet-fasterrcnn faster-rcnn fasterrcnn fasterrcnn-resnet50-fpn fasterrcnn-resnet50-fpn-v2 mobilenet-fasterrcnn object-detection pytorch resnet50-fasterrcnn squeezenet-fasterrcnn
Last synced: 5 days ago
JSON representation
PyTorch Faster R-CNN Object Detection on Custom Dataset
- Host: GitHub
- URL: https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline
- Owner: sovit-123
- License: mit
- Created: 2021-11-02T00:36:47.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-20T02:00:22.000Z (5 months ago)
- Last Synced: 2025-02-03T12:11:22.136Z (12 days ago)
- Topics: darknet-fasterrcnn, deep-learning, efficientnet-fasterrcnn, faster-rcnn, fasterrcnn, fasterrcnn-resnet50-fpn, fasterrcnn-resnet50-fpn-v2, mobilenet-fasterrcnn, object-detection, pytorch, resnet50-fasterrcnn, squeezenet-fasterrcnn
- Language: Jupyter Notebook
- Homepage:
- Size: 12.5 MB
- Stars: 230
- Watchers: 7
- Forks: 77
- Open Issues: 69
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# A Simple Pipeline to Train PyTorch FasterRCNN Model
Train PyTorch FasterRCNN models easily on any custom dataset. Choose between official PyTorch models trained on COCO dataset, or choose any backbone from Torchvision classification models, or even write your own custom backbones.
***You can run a Faster RCNN model with Mini Darknet backbone and Mini Detection Head at more than 150 FPS on an RTX 3080***.
## Get Started
[](https://colab.research.google.com/drive/1oFxPpBeE8SzSQq7BTUv28IIqQeiHHLdj?usp=sharing) [](https://www.kaggle.com/code/sovitrath/custom-faster-rcnn-training-kaggle/notebook)
* [Find blog posts/tutorials on DebuggerCafe](#Tutorials)
## Updates
* **August 28 2024:** SAHI image inference for all pretrained Torchvision Faster RCNN models integrated. [Find the script here](https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline/blob/main/sahi_inference.py).
* Filter classes to visualize during inference using the `--classes` command line argument with space separated class indices from the dataset YAML file.
For example, to visualize only persons in COCO dataset, use, `python inference.py --classes 1 `
To visualize person and car, use, `python inference.py --classes 1 3 `
* Added Deep SORT Real-Time tracking to `inference_video.py` and `onnx_video_inference.py`. Using `--track` command with the usual inference command. Support for **MobileNet** Re-ID for now.
## Custom Model Naming Conventions
***For this repository:***
* **Small head refers to 512 representation size in the Faster RCNN head and predictor.**
* **Tiny head refers to 256 representation size in the Faster RCNN head and predictor.**
* **Nano head refers to 128 representation size in the Faster RCNN head and predictor.**
## [Check All Available Model Flags](#A-List-of-All-Model-Flags-to-Use-With-the-Training-Script)
## Go To
* [Setup on Ubuntu](#Setup-for-Ubuntu)
* [Setup on Windows](#Setup-on-Windows)
* [Train on Custom Dataset](#Train-on-Custom-Dataset)
* [Inference](#Inference)
* [Evaluation](#Evaluation)
* [Available Models](#A-List-of-All-Model-Flags-to-Use-With-the-Training-Script)
* [Tutorials](#Tutorials)
## Setup on Ubuntu
1. Clone the repository.
```bash
git clone https://github.com/sovit-123/fastercnn-pytorch-training-pipeline.git
```
2. Install requirements.
1. **Method 1**: If you have CUDA and cuDNN set up already, do this in your environment of choice.
```bash
pip install -r requirements.txt
```
2. **Method 2**: If you want to install PyTorch with CUDA Toolkit in your environment of choice.
```bash
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
```
OR
```bash
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
```
OR install the version with CUDA support as per your choice from **[here](https://pytorch.org/get-started/locally/)**.
Then install the remaining **[requirements](https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline/blob/main/requirements.txt)**.
## Setup on Windows
1. **First you need to install Microsoft Visual Studio from [here](https://my.visualstudio.com/Downloads?q=Visual%20Studio%202017)**. Sing In/Sing Up by clicking on **[this link](https://my.visualstudio.com/Downloads?q=Visual%20Studio%202017)** and download the **Visual Studio Community 2017** edition.
Install with all the default chosen settings. It should be around 6 GB. Mainly, we need the C++ Build Tools.
2. Then install the proper **`pycocotools`** for Windows.
```bash
pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI
```
3. Clone the repository.
```bash
git clone https://github.com/sovit-123/fastercnn-pytorch-training-pipeline.git
```
4. Install PyTorch with CUDA support.
```bash
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
```
OR
```bash
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
```
OR install the version with CUDA support as per your choice from **[here](https://pytorch.org/get-started/locally/)**.
Then install the remaining **[requirements](https://github.com/sovit-123/pytorch-efficientdet-api/blob/main/requirements.txt)** except for `pycocotools`.
## Train on Custom Dataset
Taking an exmaple of the [smoke dataset](https://www.kaggle.com/didiruh/smoke-pascal-voc) from Kaggle. Let's say that the dataset is in the `data/smoke_pascal_voc` directory in the following format. And the `smoke.yaml` is in the `data_configs` directory. Assuming, we store the smoke data in the `data` directory
```bash
├── data
│ ├── smoke_pascal_voc
│ │ ├── archive
│ │ │ ├── train
│ │ │ └── valid
│ └── README.md
├── data_configs
│ └── smoke.yaml
├── models
│ ├── create_fasterrcnn_model.py
│ ...
│ └── __init__.py
├── outputs
│ ├── inference
│ └── training
│ ...
├── readme_images
│ ...
├── torch_utils
│ ├── coco_eval.py
│ ...
├── utils
│ ├── annotations.py
│ ...
├── datasets.py
├── inference.py
├── inference_video.py
├── __init__.py
├── README.md
├── requirements.txt
└── train.py
```
The content of the `smoke.yaml` should be the following:
```yaml
# Images and labels direcotry should be relative to train.py
TRAIN_DIR_IMAGES: ../../xml_od_data/smoke_pascal_voc/archive/train/images
TRAIN_DIR_LABELS: ../../xml_od_data/smoke_pascal_voc/archive/train/annotations
# VALID_DIR should be relative to train.py
VALID_DIR_IMAGES: ../../xml_od_data/smoke_pascal_voc/archive/valid/images
VALID_DIR_LABELS: ../../xml_od_data/smoke_pascal_voc/archive/valid/annotations
# Class names.
CLASSES: [
'__background__',
'smoke'
]
# Number of classes (object classes + 1 for background class in Faster RCNN).
NC: 2
# Whether to save the predictions of the validation set while training.
SAVE_VALID_PREDICTION_IMAGES: True
```
***Note that*** *the data and annotations can be in the same directory as well. In that case, the TRAIN_DIR_IMAGES and TRAIN_DIR_LABELS will save the same path. Similarly for VALID images and labels. The `datasets.py` will take care of that*.
Next, to start the training, you can use the following command.
**Command format:**
```bash
python train.py --data --epochs 100 --model --name --batch 16
```
**In this case, the exact command would be:**
```bash
python train.py --data data_configs/smoke.yaml --epochs 100 --model fasterrcnn_resnet50_fpn --name smoke_training --batch 16
```
**The terimal output should be similar to the following:**
```
Number of training samples: 665
Number of validation samples: 72
3,191,405 total parameters.
3,191,405 training parameters.
Epoch 0: adjusting learning rate of group 0 to 1.0000e-03.
Epoch: [0] [ 0/84] eta: 0:02:17 lr: 0.000013 loss: 1.6518 (1.6518) time: 1.6422 data: 0.2176 max mem: 1525
Epoch: [0] [83/84] eta: 0:00:00 lr: 0.001000 loss: 1.6540 (1.8020) time: 0.0769 data: 0.0077 max mem: 1548
Epoch: [0] Total time: 0:00:08 (0.0984 s / it)
creating index...
index created!
Test: [0/9] eta: 0:00:02 model_time: 0.0928 (0.0928) evaluator_time: 0.0245 (0.0245) time: 0.2972 data: 0.1534 max mem: 1548
Test: [8/9] eta: 0:00:00 model_time: 0.0318 (0.0933) evaluator_time: 0.0237 (0.0238) time: 0.1652 data: 0.0239 max mem: 1548
Test: Total time: 0:00:01 (0.1691 s / it)
Averaged stats: model_time: 0.0318 (0.0933) evaluator_time: 0.0237 (0.0238)
Accumulating evaluation results...
DONE (t=0.03s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.001
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.002
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.007
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.029
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.074
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.028
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.088
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.167
SAVING PLOTS COMPLETE...
...
Epoch: [4] [ 0/84] eta: 0:00:20 lr: 0.001000 loss: 0.9575 (0.9575) time: 0.2461 data: 0.1662 max mem: 1548
Epoch: [4] [83/84] eta: 0:00:00 lr: 0.001000 loss: 1.1325 (1.1624) time: 0.0762 data: 0.0078 max mem: 1548
Epoch: [4] Total time: 0:00:06 (0.0801 s / it)
creating index...
index created!
Test: [0/9] eta: 0:00:02 model_time: 0.0369 (0.0369) evaluator_time: 0.0237 (0.0237) time: 0.2494 data: 0.1581 max mem: 1548
Test: [8/9] eta: 0:00:00 model_time: 0.0323 (0.0330) evaluator_time: 0.0226 (0.0227) time: 0.1076 data: 0.0271 max mem: 1548
Test: Total time: 0:00:01 (0.1116 s / it)
Averaged stats: model_time: 0.0323 (0.0330) evaluator_time: 0.0226 (0.0227)
Accumulating evaluation results...
DONE (t=0.03s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.137
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.313
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.118
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.029
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.175
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.204
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.306
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.140
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.683
SAVING PLOTS COMPLETE...
```
## Distributed Training
**Training on 2 GPUs**:
```bash
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 --use_env train.py --data data_configs/smoke.yaml --epochs 100 --model fasterrcnn_resnet50_fpn --name smoke_training --batch 16
```
## Inference
### Image Inference on COCO Pretrained Model
By default using **Faster RCNN ResNet50 FPN V2** model.
```bash
python inference.py
```
Use model of your choice with an image input.
```bash
python inference.py --model fasterrcnn_mobilenetv3_large_fpn --input example_test_data/image_1.jpg
```
### Image Inference in Custom Trained Model
In this case you only need to give the weights file path and input file path. The config file and the model name are optional. If not provided they will will be automatically inferred from the weights file.
```bash
python inference.py --input data/inference_data/image_1.jpg --weights outputs/training/smoke_training/last_model_state.pth
```
### Video Inference on COCO Pretrrained Model
```bash
python inference_video.py
```
### Video Inference in Custom Trained Model
```bash
python inference_video.py --input data/inference_data/video_1.mp4 --weights outputs/training/smoke_training/last_model_state.pth
```
### Tracking using COCO Pretrained Models
```bash
# Track all COCO classes (Faster RCNN ResNet50 FPN V2).
python inference_video.py --track --model fasterrcnn_resnet50_fpn_v2 --show
# Track all COCO classes (Faster RCNN ResNet50 FPN V2) using own video.
python inference_video.py --track --model fasterrcnn_resnet50_fpn_v2 --show --input ../inference_data/video_1.mp4
# Tracking only person class (index 1 in COCO pretrained). Check `COCO_91_CLASSES` attribute in `data_configs/coco.yaml` for more information.
python inference_video.py --track --model fasterrcnn_resnet50_fpn_v2 --show --input ../inference_data/video_4.mp4 --classes 1
# Tracking only person and car classes (indices 1 and 3 in COCO pretrained). Check `COCO_91_CLASSES` attribute in `data_configs/coco.yaml` for more information.
python inference_video.py --track --model fasterrcnn_resnet50_fpn_v2 --show --input ../inference_data/video_4.mp4 --classes 1 3
# Tracking using custom trained weights. Just provide the path to the weights instead of model name.
python inference_video.py --track --weights outputs/training/fish_det/best_model.pth --show --input ../inference_data/video_6.mp4
```
## Evaluation
Replace the required arguments according to your need.
```bash
python eval.py --model fasterrcnn_resnet50_fpn_v2 --weights outputs/training/trial/best_model.pth --data data_configs/aquarium.yaml --batch 4
```
You can use the following command to show a table for **class-wise Average Precision** (`--verbose` additionally needed).
```bash
python eval.py --model fasterrcnn_resnet50_fpn_v2 --weights outputs/training/trial/best_model.pth --data data_configs/aquarium.yaml --batch 4 --verbose
```
## A List of All Model Flags to Use With the Training Script
The following command expects the `coco` dataset to be present one directory back inside the `input` folder in XML format. You can find the dataset [here on Kaggle](https://www.kaggle.com/datasets/sovitrath/coco-xml-format). Check the `data_configs/coco.yaml` for more details. You can change the relative dataset path in the YAML file according to your structure.
```bash
# Usage
python train.py --model fasterrcnn_resnet50_fpn_v2 --data data_configs/coco.yaml
```
**OR USE ANY ONE OF THE FOLLOWING**
```
[
'fasterrcnn_convnext_small',
'fasterrcnn_convnext_tiny',
'fasterrcnn_custom_resnet',
'fasterrcnn_darknet',
'fasterrcnn_efficientnet_b0',
'fasterrcnn_efficientnet_b4',
'fasterrcnn_mbv3_small_nano_head',
'fasterrcnn_mbv3_large',
'fasterrcnn_mini_darknet_nano_head',
'fasterrcnn_mini_darknet',
'fasterrcnn_mini_squeezenet1_1_small_head',
'fasterrcnn_mini_squeezenet1_1_tiny_head',
'fasterrcnn_mobilenetv3_large_320_fpn', # Torchvision COCO pretrained
'fasterrcnn_mobilenetv3_large_fpn', # Torchvision COCO pretrained
'fasterrcnn_nano',
'fasterrcnn_resnet18',
'fasterrcnn_resnet50_fpn_v2', # Torchvision COCO pretrained
'fasterrcnn_resnet50_fpn', # Torchvision COCO pretrained
'fasterrcnn_resnet101',
'fasterrcnn_resnet152',
'fasterrcnn_squeezenet1_0',
'fasterrcnn_squeezenet1_1_small_head',
'fasterrcnn_squeezenet1_1',
'fasterrcnn_vitdet',
'fasterrcnn_vitdet_tiny',
'fasterrcnn_mobilevit_xxs',
'fasterrcnn_regnet_y_400mf'
]
```
## Tutorials
* [Wheat Detection using Faster RCNN and PyTorch](https://debuggercafe.com/wheat-detection-using-faster-rcnn-and-pytorch/)
* [Plant Disease Detection using the PlantDoc Dataset and PyTorch Faster RCNN](https://debuggercafe.com/plant-disease-detection-using-plantdoc/)
* [Small Scale Traffic Light Detection using PyTorch](https://debuggercafe.com/small-scale-traffic-light-detection/)