https://github.com/sovit-123/pytorch-efficientdet-api
A PyTorch EfficientDet API for easy training and inference on custom datasets.
https://github.com/sovit-123/pytorch-efficientdet-api
deep-learning deeplearning efficientdet efficientnet object-detection objectdetection pytorch
Last synced: 28 days ago
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A PyTorch EfficientDet API for easy training and inference on custom datasets.
- Host: GitHub
- URL: https://github.com/sovit-123/pytorch-efficientdet-api
- Owner: sovit-123
- Created: 2022-01-22T16:27:35.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-27T06:54:42.000Z (about 3 years ago)
- Last Synced: 2025-02-03T10:46:20.827Z (3 months ago)
- Topics: deep-learning, deeplearning, efficientdet, efficientnet, object-detection, objectdetection, pytorch
- Language: Python
- Homepage: https://sovit-123.github.io/pytorch-efficientdet-api/
- Size: 1.11 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PyTorch EfficientDet API
A simple training, testing, and inference pipeline using [Ross Wightman's EfficientDet models](https://github.com/rwightman/efficientdet-pytorch). Ross Wightman's repo is used a submodule to load the EfficientDet models.
The training/testing/inference code are custom written.
Get started with training within 5 minutes if you have the images and XML annotation files.
## Get Started with Inference
[](https://colab.research.google.com/drive/1-_g9yqE3DA4Q3r2Tw1jRG86SCshIAOx1?usp=sharing) [](https://www.kaggle.com/sovitrath/pytorch-efficientdet-api-coco-model-inference)
## Go To
* [Setup for Ubuntu](#Setup-for-Ubuntu)
* [Setup on Windows](#Setup-on-Windows)
* [Train on Custom Dataset](#Train-on-Custom-Dataset)
* [Inference](#Inference)
## Setup for Ubuntu
1. Clone the repository.
```
git clone --recursive https://github.com/sovit-123/pytorch-efficientdet-api.git
```
2. Install requirements.
1. **Method 1**: If you have CUDA and cuDNN set up already, do this in your environment of choice
```
pip install -r requirments.txt
```
2. **Method 2**: If you want to install PyTorch with CUDA Toolkit in your environment of choice.
```
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
```
OR
```
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)**.
## 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.
```
pip install git+https://github.com/gautamchitnis/cocoapi.git@cocodataset-master#subdirectory=PythonAPI
```
3. Clone the repository.
```
git clone --recursive https://github.com/sovit-123/pytorch-efficientdet-api.git
```
4. Install PyTorch with CUDA support.
```
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
```
OR
```
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.
```
├── data
│ ├── smoke_pascal_voc
│ │ ├── archive
│ │ │ ├── train
│ │ │ └── valid
│ └── README.md
├── data_configs
│ └── smoke.yaml
├── efficientdet-pytorch
│ ├── effdet
│ ...
├── model_configs
│ └── model_config.yaml
├── models
│ ├── efficientdet_d0.py
│ ├── efficientdet_model.py
│ └── tf_efficientdet_lite0.py
├── outputs
│ ├── inference
│ │ ├── res_1
│ │ └── res_2
│ └── training
│ ├── res_1
│ └── res_2
├── torch_utils
│ ├── coco_eval.py
│ ├── coco_utils.py
│ ├── engine.py
│ ├── README.md
│ └── utils.py
├── config.py
├── custom_utils.py
├── datasets.py
├── README.md
├── requirements.txt
├── test_image.py
├── test_video.py
└── train.py
```
The content of the `smoke.yaml` should be the following:
```yaml
# TRAIN_DIR should be relative to train.py
TRAIN_DIR_IMAGES: data/smoke_pascal_voc/archive/train/images
TRAIN_DIR_LABELS: data/smoke_pascal_voc/archive/train/annotations
# VALID_DIR should be relative to train.py
VALID_DIR_IMAGES: data/smoke_pascal_voc/archive/valid/images
VALID_DIR_LABELS: data/smoke_pascal_voc/archive/valid/annotations
# Class names.
CLASSES: ['smoke']
# Number of classes.
NC: 1
# 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:**
```
python train.py --model --config --device --epochs --workers --batch-size
```
**In this case, the exact command would be:**
```
python train.py --model tf_efficientdet_lite0 --config data_configs/smoke.yaml --device cuda:0 --epochs 5 --workers 4 --batch-size 8
```
**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...
```
## Inference
### Inference on Images using Pretrained Models
Use the **[efficientdet-pytorch](https://github.com/rwightman/efficientdet-pytorch)** models trained on the COCO dataset.
**Command format:**
```
python test_image.py --input --model
```
**Example:**
```
python test_image.py --input data/inference_data/image_1.jpg --model tf_efficientdet_lite0
```
### Inference on Images using Custom Trained Model
Use your custom trained model to run inference on any image. Providing path to config file is mandatory here to get class information
**Command format:**
```
python test_image.py --input --model --weights --config
```
**Example:**
```
python test_image.py --input data/inference_data/image_1.jpg --model tf_efficientdet_lite0 --weights outputs/training/res_19/last_model_state.pth --config data_configs/smoke.yaml
```
### Inference on Videos using Pretrained Models
**Command format:**
```
python test_video.py --input --model
```
**Example:**
```
python test_video.py --input data/inference_data/video_2.mp4 --model tf_efficientdet_lite0
```
### Inference on Videos using Custom Trained Models
**Command format:**
```
python test_video.py --input --model --weights --config
```
**Example:**
```
python test_video.py --input data/inference_data/video_3.mp4 --model tf_efficientdet_lite0 --weights outputs/training/res_19/last_model_state.pth --config data_configs/smoke.yaml
```
###