add readme and model weights

LICENSE

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+MIT License
+
+Copyright (c) 2021 Brian
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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----
-license: afl-3.0
----
+# Face Detection using DEtection TRansformers from Facebook AI 🚀
+![PyTorch 1.5 +](https://img.shields.io/badge/Pytorch-1.5%2B-green)
+![torch vision 0.6 +](https://img.shields.io/badge/torchvision%20-0.6%2B-green)
+
+This repository includes 
+* Training Pipeline for DETR on Custom dataset
+* Wider Face Dataset annotaions and images
+* Evaluation on test dataset 
+* Trained weights for Wider Face Dataset in [release page](https://github.com/NyanSwanAung/Pothole-Detection-using-MaskRCNN/releases)
+* Metrics Visualization 
+
+## About Model 
+DETR or DEtection TRansformer is Facebook’s newest addition to the market of available deep learning-based object detection solutions. Very simply, it utilizes the transformer architecture to generate predictions of objects and their position in an image. DETR is a joint Convolutional Neural Network (CNN) and Transformer with a feed-forward network as a head. This architecture allows the network to reliably reason about object relations in the image using the powerful multi-head attention mechanism inherent in the Transformer architecture using features extracted by the CNN.
+
+![DETR Architecutre](https://miro.medium.com/max/1200/1*niV3pN0JvipfJeqmdWN-3g.png)
+
+## Face Dataset
+
+![Dataset Image](http://shuoyang1213.me/WIDERFACE/support/intro.jpg)
+
+I've used [WIDER FACE dataset](http://shuoyang1213.me/WIDERFACE/) which is a publicly available face detection benchmark dataset, consisting of 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, the original dataset was split into 40%/10%/50% as training, validation and testing sets. 
+
+By compiling the give code, the dataset will be automatically downloaded but you can download it manually from the official website or from my github [release page](https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/releases). 
+
+In [dataloader/face.py](https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/blob/main/dataloaders/face.py), I set the maximum width of images in the random transform to 800 pixels. This should allow for training on most GPUs, but it is advisable to change back to the original 1333 if your GPU can handle it.
+
+## Model 
+
+We're going to use **DETR with a backbone of Resnet 50**, pretrained on COCO 2017 dataset. AP is computed on COCO 2017 val5k, and inference time is over the first 100 val5k COCO images, with torchscript transformer. If you want to use other DETR models, you can find them in model zoo below.
+
+Model Zoo
+
+<table>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>name</th>
+      <th>backbone</th>
+      <th>schedule</th>
+      <th>inf_time</th>
+      <th>box AP</th>
+      <th>url</th>
+      <th>size</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>DETR</td>
+      <td>R50</td>
+      <td>500</td>
+      <td>0.036</td>
+      <td>42.0</td>
+      <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r50_log.txt">logs</a></td>
+      <td>159Mb</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>DETR-DC5</td>
+      <td>R50</td>
+      <td>500</td>
+      <td>0.083</td>
+      <td>43.3</td>
+      <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-f0fb7ef5.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r50-dc5_log.txt">logs</a></td>
+      <td>159Mb</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>DETR</td>
+      <td>R101</td>
+      <td>500</td>
+      <td>0.050</td>
+      <td>43.5</td>
+      <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r101_log.txt">logs</a></td>
+      <td>232Mb</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>DETR-DC5</td>
+      <td>R101</td>
+      <td>500</td>
+      <td>0.097</td>
+      <td>44.9</td>
+      <td><a href="https://dl.fbaipublicfiles.com/detr/detr-r101-dc5-a2e86def.pth">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detr/logs/detr-r101-dc5_log.txt">logs</a></td>
+      <td>232Mb</td>
+    </tr>
+  </tbody>
+</table>
+
+## Training and Evaluation Steps
+
+Run all the cells of [detr_custom_dataset.ipynb](https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/blob/main/DETR_custom_dataset.ipynb) to train your model without any errors in Google Colaboratory.
+
+Follow this [readme](https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/blob/main/TRAINING-and-INFERENCING.md) to understand the training pipeline of DETR and evaluation on test images.
+
+## Results
+
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/results1.png)
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/results2.png)
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/results3.png)
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/results4.png)
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/results5.png)
+
+
+## COCO Evaluation Metrics on Validation Dataset (After 15 epochs of training)
+
+It took me 4:59:45 hours to finish 15 epochs with batch_size=16 using Tesla P100-PCIE. If you want better accuracy, you can train more epochs.
+
+```bash
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.393
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.766
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.370
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.055
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.615
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.201
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.448
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.500
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.194
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.706
+```
+## Metrics Visualization 
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/metrics1.png)
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/metrics2.png)
+![](https://raw.githubusercontent.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/main/assets/metrics3.png)
+
+## Augmentation methods 
+For train images, 
+``` 
+T.RandomHorizontalFlip(),
+            T.RandomSelect(
+                T.RandomResize(scales, max_size=800),
+                T.Compose([
+                    T.RandomResize([400, 500, 600]),
+                    T.RandomSizeCrop(384, 600),
+                    T.RandomResize(scales, max_size=800),
+                ])
+```
+
+For val images, 
+
+``` T.RandomResize([800], max_size=800) ```
+
+## References
+
+[DETR Tutorial by thedeepreader](https://github.com/thedeepreader/detr_tutorial)
+
+[Training DETR on your own dataset by Oliver Gyldenberg Hjermitslev](https://towardsdatascience.com/training-detr-on-your-own-dataset-bcee0be05522)
+
+[Facebook AI's original DETR repo](https://github.com/facebookresearch/detr)

TRAINING-and-EVALUATION.md

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+## Training Steps
+
+In this [notebook](https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/blob/main/detr_custom_dataset.ipynb), you'll need to follow 4 steps in order to train the model
+
+1. Prepare prerequisite
+2. Setup Paths
+3. Train Model
+
+Note: If you want to edit and add new codes to this notebook, I suggest clone the repo or save the notebook as a copy or else you'll lose your new codes.
+
+### Step 1 - Prepare Prerequisites 
+
+1.1 Clone my github repository
+```python
+!git clone https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset.git
+%cd Object-Detection-Using-DETR-CustomDataset/
+```
+
+1.2 Download face dataset from my github release page using wget
+```bash
+# Download train images
+!wget https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/releases/download/v1.0/WIDER_train.zip -O datasets/train.zip
+
+# Download val images
+!wget https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/releases/download/v1.0/WIDER_val.zip -O datasets/val.zip
+
+# Download annotations
+!wget https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/releases/download/v1.0/wider_face_split.zip -O datasets/annotations.zip
+```
+
+1.3 There is an already implemented dataloader and you just need to copy face dataloader to detr/datasets folder. You can modify the dataloader if you want.
+```bash
+!cp dataloaders/face.py /content/Object-Detection-Using-DETR-CustomDataset/detr/datasets 
+
+# Make folder for trained-weights.
+!mkdir detr/outputs
+```
+
+1.4 Install COCO API for evaluation 
+```bash
+!pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
+```
+
+The original Wider Face Dataset contains images and .mat for annotations. In order to train DETR, we need the dataset to be in COCO format below like this.
+```
+--annotations  # annotation json files
+  |---- train.json
+  |---- val.json
+--train # train images
+  |---- image0
+  |---- image1
+  |---- ....
+--val      # val images
+  |---- image0
+  |---- image1
+  |---- ....
+```
+
+1.5 Dataset Prepartion 
+
+There is a script [datasets/face_to_coco.py](https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/blob/main/datasets/face_to_coco.py) which converts wider face dataset into COCO formatted dataset. Just simply run the cell and you will get train.json and val.json.
+
+Before running the script, you need to set your dataset path in line 27 and 28 of the script.
+```python
+25.      phases = ["train", "val"]
+26.      for phase in phases:
+27.         root_path = "datasets/WIDER_{}/images/".format(phase)
+28.         gt_path = os.path.join("datasets/wider_face_split/wider_face_{}.mat".format(phase))
+```
+
+1.6 After setting up, run the script **face_to_coco.py**
+
+```bash
+!python datasets/face_to_coco.py
+
+# Move json files to datasets folder
+!mv train.json datasets && mv val.json datasets
+```
+
+After successful data preparation, you will get datasets folder like this
+```
+datasets
+  |---- WIDER_train
+  |---- WIDER_val
+  |---- WIDER_test
+  |---- wider_face_split
+  |---- train.json
+  |---- val.json
+```
+
+### Step 2 - Setup Paths
+```python
+DATASET_PATH = 'datasets'
+
+TRAIN_IMG = 'datasets/WIDER_train/images'
+TRAIN_JSON = 'datasets/train.json'
+
+VAL_IMG = 'datasets/WIDER_val/images'
+VAL_JSON = 'datasets/val.json'
+```
+
+### Step 3 - Train Model
+
+To train the model, you'll need to run **detr/main.py** from with required arguments. If you want to see supported arguments and the definition of arguments, run below code.
+```
+!python detr/main.py --help
+```
+
+If you want to train model with my configurations, run the code below.
+
+```
+!python detr/main.py \
+    --batch_size=16 \
+    --epochs=15 \
+    --num_classes=2 \
+    --num_queries=100 \
+    --dataset_file='face' \
+    --data_path={DATASET_PATH} \
+    --train_folder={TRAIN_IMG} \
+    --train_json={TRAIN_JSON} \
+    --val_folder={VAL_IMG} \
+    --val_json={VAL_JSON} \
+    --output_dir='detr/outputs' \
+    --resume={PRETRAINED_MODEL}
+```
+
+## Evaluation Steps
+
+To evaluate your model, you can use own trained weights or download my trained weights from github release page.
+
+```bash
+!wget https://github.com/NyanSwanAung/Object-Detection-Using-DETR-CustomDataset/releases/download/v1.0/detr_r50_ep15.tar
+
+# Extract tar file
+!tar -xf detr_r50_ep15.tar
+```
+
+Load DETR model from torch.hub and load ckpt file into model
+
+```python
+TRAINED_CKPT_PATH = 'YOUR CHECKPOINT PATH' # sample --> checkpoint.pth
+checkpoint = torch.load(TRAINED_CKPT, map_location='cpu')
+model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=False, num_classes=2)
+model.load_state_dict(checkpoint['model'], strict=False)
+```
+For simplicity, I've modified postprocessing function from Standalone Colab Notebook of original detr repo.
+
+```python
+def postprocess_img(img_path): 
+  im = Image.open(img_path)
+
+  # mean-std normalize the input image (batch-size: 1)
+  img = transform(im).unsqueeze(0)
+
+  # propagate through the model
+  start = time.time()
+  outputs = model(img)
+  end = time.time()
+  print(f'Prediction time per image: {math.ceil(end - start)}s ', )
+
+  # keep only predictions with 0.7+ confidence
+  probas = outputs['pred_logits'].softmax(-1)[0, :, :-1]
+  keep = probas.max(-1).values > 0.9
+
+  # convert boxes from [0; 1] to image scales
+  bboxes_scaled = rescale_bboxes(outputs['pred_boxes'][0, keep], im.size)
+  
+  plot_results(im, probas[keep], bboxes_scaled)
+```
+
+Load paths from test folder
+```python
+
+TEST_IMG_PATH = 'datasets/WIDER_test/images'
+
+img_format = {'jpg', 'png', 'jpeg'}
+paths = list()
+
+for obj in os.scandir(TEST_IMG_PATH):
+  if obj.is_dir():
+    paths_temp = [obj.path for obj in os.scandir(obj.path) if obj.name.split(".")[-1] in img_format]
+    print()
+    paths.extend(paths_temp)
+```
+
+Evaluate on first 10 images
+```python
+for i in paths[1:10]:
+  postprocess_img(i)
+```
+## Metrics Visulaization 
+After training detr, you will get output folder containing log.txt. By using that output folder, you can visualize your metrics. 
+```
+from detr.util.plot_utils import plot_logs
+from pathlib import Path
+
+log_directory = [Path('trained-weights')]
+
+fields_of_interest = ('loss', 'mAP', 'loss_ce', 'loss_bbox', 'loss_giou', 'class_error', 'cardinality_error_unscaled')
+plot_logs(log_directory, fields_of_interest)
+```
+
+
+

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detr_r50_ep15/log.txt

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