# ObjectDetection

**Repository Path**: voldemort/ObjectDetection

## Basic Information

- **Project Name**: ObjectDetection
- **Description**: Some experiments with object detection in PyTorch
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-17
- **Last Updated**: 2022-07-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ObjectDetection
Some experiments with object detection in PyTorch and [FastAi](https://www.fast.ai/).
This repo is created for educational reasons and to get a deeper understanding of [RetinaNet](https://arxiv.org/abs/1708.02002) and object detection general. If you like it, please let me know, if you find any bugs or tips for improvements also. 

# Install

```
pip install object-detection-fastai
```

Test: [Coco Colab](https://colab.research.google.com/drive/1qUEy1w8uYT2PQhan77RIn8NhfE_bMk63)

# Image annotation 

This paper describes the EXACT-Server in-depth, EXACT enables you to annotate your data and train an object detection model with this repository. Please cite if you use this tool in your research:

Marzahl et al. [EXACT: A collaboration toolset for algorithm-aided annotation of almost everything](https://arxiv.org/abs/2004.14595) 

```
@misc{marzahl2020exact,
    title={EXACT: A collaboration toolset for algorithm-aided annotation of almost everything},
    author={Christian Marzahl and Marc Aubreville and Christof A. Bertram and Jennifer Maier and Christian Bergler and Christine Kröger and Jörn Voigt and Robert Klopfleisch and Andreas Maier},
    year={2020},
    eprint={2004.14595},
    archivePrefix={arXiv},
    primaryClass={cs.HC}
}
```


# Update old code

```python
# Old imports:
from helper.object_detection_helper import *
from loss.RetinaNetFocalLoss import RetinaNetFocalLoss
from models.RetinaNet import RetinaNet
from callbacks.callbacks import BBLossMetrics, BBMetrics, PascalVOCMetric

# New imports
from object_detection_fastai.helper.object_detection_helper import *
from object_detection_fastai.loss.RetinaNetFocalLoss import RetinaNetFocalLoss
from object_detection_fastai.models.RetinaNet import RetinaNet
from object_detection_fastai.callbacks.callbacks import BBLossMetrics, BBMetrics, PascalVOCMetric
```


[![RetinaNet WSI](http://img.youtube.com/vi/xCcdVgV1rRA/0.jpg)](https://www.youtube.com/watch?v=xCcdVgV1rRA "RetinaNet WSI")

The basline for this notebook was created by [Sylvain Gugger](https://github.com/sgugger) from FastAi [DevNotebook](https://github.com/fastai/fastai_docs/blob/master/dev_nb/102a_coco.ipynb). Thank you very much, it was a great starting point and I'm a big fan off your work.


# Publications using this code:

[x] [Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides](https://arxiv.org/abs/1908.04767)

# Examples:
- [Medical images](object_detection_fastai/examples/Cells_Retina_Net.ipynb)
- [Coco subset](object_detection_fastai/examples/CocoTiny_Retina_Net.ipynb)
- [Coco Colab](object_detection_fastai/https://colab.research.google.com/drive/1qUEy1w8uYT2PQhan77RIn8NhfE_bMk63)

# Results:

![Cell detection](Images/Cells1.png "Cell detection")
![Coco Chair](Images/Chair.png "Chair")
![Coco Couch](Images/Couch.png "Couch")
![Coco Vase](Images/Vase.png "Vase")

# Features:

[x] Coco Metric at train time via callback 
![Coco Metrics](Images/TrainCocoMetrics.png "Metrics")
[x] Flexibility
```python
# use the feature map sizes 32,18,8,4 with 32 channels and two conv layers for detection and classification
RetinaNet(encoder, n_classes=data.train_ds.c, n_anchors=18, sizes=[32,16,8,4], chs=32, final_bias=-4., n_conv=2)
'''
  (classifier): Sequential(
    (0): Sequential(
      (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): ReLU()
    )
    (1): Sequential(
      (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): ReLU()
    )
    (2): Conv2d(32, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
```

```python
# use the feature map sizes 32 with 8 channels and three conv layers for detection and classification
RetinaNet(encoder, n_classes=data.train_ds.c, n_anchors=3, sizes=[32], chs=8, final_bias=-4., n_conv=3)
```

[x] Debug anchor matches for training.


On the left image we see objects that are represented by anchors.
On the right objects with no corresponding anchors for training.
![Anchors](Images/anchors.png "anchors")
The size of the smallest anchors should be further decreased to match the small objects on the right image.