add resnet regressor model

.gitignore

@@ -259,4 +259,6 @@ fabric.properties
 ## Folder
 /3DBBox
 /eval
-/weights
+/weights
+/sandbox
+/dataset

CONTRIBUTING.md

@@ -42,17 +42,17 @@ changes** button. All done, your PR is now submitted to YOLOv5 for review and ap
 To allow your work to be integrated as seamlessly as possible, we advise you to:
 
 - ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an
-  automatic [GitHub Actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) merge may
-  be attempted by writing /rebase in a new comment, or by running the following code, replacing 'feature' with the name
-  of your local branch:
-
-```bash
-git remote add upstream https://github.com/ultralytics/yolov5.git
-git fetch upstream
-# git checkout feature  # <--- replace 'feature' with local branch name
-git merge upstream/master
-git push -u origin -f
-```
+  automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may
+  be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature'
+  with the name of your local branch:
+
+  ```bash
+  git remote add upstream https://github.com/ultralytics/yolov5.git
+  git fetch upstream
+  git checkout feature  # <----- replace 'feature' with local branch name
+  git merge upstream/master
+  git push -u origin -f
+  ```
 
 - ✅ Verify all Continuous Integration (CI) **checks are passing**.
 - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase

Dockerfile

@@ -9,10 +9,10 @@ RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
 # Install python dependencies
 COPY requirements.txt .
 RUN python -m pip install --upgrade pip
-RUN pip uninstall -y torch torchvision torchtext
-RUN pip install --no-cache -r requirements.txt albumentations wandb gsutil notebook \
-    torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
-# RUN pip install --no-cache -U torch torchvision
+RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
+RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook wandb>=0.12.2
+RUN pip install --no-cache -U torch torchvision numpy Pillow
+# RUN pip install --no-cache torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
 
 # Create working directory
 RUN mkdir -p /usr/src/app

README.md

@@ -1,31 +1,21 @@
-# YOLO3D
-3D Object Detection with YOLOv5 for Autonomous Driving Vehicle
+# YOLO For 3D Object Detection
 
-## Installation
-1. Create Conda Env
-```
-conda create -n yolo3d python=3.8 numpy
-```
-2. Install pyTorch and torchvision
-Download `.whl` from [Nelson Liu](https://cs.stanford.edu/~nfliu/files/pytorch/whl/torch_stable.html) for unsupported GPU (old GPU). Install with pip:
-```
-pip install ./torch-1.8.1+cu101-cp38-cp38-linux_x86_64.whl
-pip install ./torchvision-0.9.1+cu101-cp38-cp38-linux_x86_64.whl
-```
-3. Install Requirements
-```
-pip install -r requirements.txt
-```
 
-## Inference
-### Inference with `detect.py`
+## Training 
 ```
-python detect.py \
-    --weights yolov5s.pt \
-    --source data/images \
-    --classes 0 1 2 \
-    --project runs/detect/
+python train_vgg.py \
+    --dataset_path /home/didi/Repository/RTYOLO3D/dataset/KITTI/training/ \
+    --weights /home/didi/Repository/RTYOLO3D/weights \
+    --epochs 100 \
+    --batch_size 50 \
+    --num_workers 4 \
 ```
 
-## Reference
-- [YOLOv5 by ultralytics](https://github.com/ultralytics/yolov5)
+```
+python train_vgg.py \
+    --dataset_path /home/didi/Repository/RTYOLO3D/dataset/KITTI/training/ \
+    --weights /home/didi/Repository/RTYOLO3D/weights \
+    --epochs 1 \
+    --batch_size 1 \
+    --num_workers 4 \
+```