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README.md

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+# Custom Training with YOLOv7 🔥
+
+## Some Important links
+- [Model Inference🤖](https://huggingface.co/spaces/owaiskha9654/Custom_Yolov7)
+- [**🚀Training Yolov7 on Kaggle**](https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset)
+- [Weight and Biases 🐝](https://wandb.ai/owaiskhan9515/YOLOR)
+- [HuggingFace 🤗 Model Repo](https://huggingface.co/owaiskha9654/Yolov7_Custom_Object_Detection)
+
+## Contact Information
+
+
+- **Name** - Owais Ahmad
+- **Phone** - +91-9515884381
+- **Email** - owaiskhan9654@gmail.com
+- **Portfolio** - https://owaiskhan9654.github.io/
+
+# Objective  
+
+## To Showcase custom Object Detection on the Given Dataset to train and Infer the Model using newly launched YoloV7.
+
+# Data Acquisition
+
+The goal of this task is to train a model that
+can localize and classify each instance of **Person** and **Car** as accurately as possible.
+
+- [Link to the Downloadable Dataset](https://www.kaggle.com/datasets/owaiskhan9654/car-person-v2-roboflow)
+
+
+```python
+from IPython.display import Markdown, display
+
+display(Markdown("../input/Car-Person-v2-Roboflow/README.roboflow.txt"))
+```
+
+# Custom Training with YOLOv7 🔥
+
+In this Notebook, I have processed the images with RoboFlow because in COCO formatted dataset was having different dimensions of image and Also data set was not splitted into different Format.
+To train a custom YOLOv7 model we need to recognize the objects in the dataset. To do so I have taken the following steps:
+
+* Export the dataset to YOLOv7
+* Train YOLOv7 to recognize the objects in our dataset
+* Evaluate our YOLOv7 model's performance
+* Run test inference to view performance of YOLOv7 model at work
+
+
+# 📦 [YOLOv7](https://github.com/WongKinYiu/yolov7)
+<div align=left><img src="https://raw.githubusercontent.com/WongKinYiu/yolov7/main/figure/performance.png" width=800>
+
+    
+**Image Credit** -  [WongKinYiu](https://github.com/WongKinYiu/yolov7)
+</div>
+# Step 1: Install Requirements
+
+
+```python
+!git clone https://github.com/WongKinYiu/yolov7 # Downloading YOLOv7 repository and installing requirements
+%cd yolov7
+!pip install -qr requirements.txt
+!pip install -q roboflow
+
+```
+
+# **Downloading YOLOV7 starting checkpoint**
+
+
+```python
+!wget "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
+```
+
+
+```python
+import os
+import glob
+import wandb
+import torch
+from roboflow import Roboflow
+from kaggle_secrets import UserSecretsClient
+from IPython.display import Image, clear_output, display  # to display images
+
+
+
+print(f"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")
+```
+
+<img src="https://camo.githubusercontent.com/dd842f7b0be57140e68b2ab9cb007992acd131c48284eaf6b1aca758bfea358b/68747470733a2f2f692e696d6775722e636f6d2f52557469567a482e706e67">
+
+> I will be integrating W&B for visualizations and logging artifacts and comparisons of different models!
+> 
+> [YOLOv7-Car-Person-Custom](https://wandb.ai/owaiskhan9515/YOLOR)
+
+
+
+```python
+try:
+    user_secrets = UserSecretsClient()
+    wandb_api_key = user_secrets.get_secret("wandb_api")
+    wandb.login(key=wandb_api_key)
+    anonymous = None
+except:
+    wandb.login(anonymous='must')
+    print('To use your W&B account,\nGo to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. \nGet your W&B access token from here: https://wandb.ai/authorize')
+    
+    
+    
+wandb.init(project="YOLOv7",name=f"7. YOLOv7-Car-Person-Custom-Run-7")
+```
+
+# Step 2: Assemble Our Dataset
+
+
+![](https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png)
+
+In order to train our custom model, we need to assemble a dataset of representative images with bounding box annotations around the objects that we want to detect. And we need our dataset to be in YOLOv7 format.
+
+In Roboflow, We can choose between two paths:
+
+* Convert an existing Coco dataset to YOLOv7 format. In Roboflow it supports over [30 formats object detection formats](https://roboflow.com/formats) for conversion.
+* Uploading only these raw images and annotate them in Roboflow with [Roboflow Annotate](https://docs.roboflow.com/annotate).
+
+
+
+# Version v7 Jan 30, 2023 Looks like this.
+
+![](https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Roboflow_train1.JPG)
+
+
+### Since paid credits are required to train the model on RoboFlow I have used Kaggle Free resources to train it here 
+
+
+
+### Note you can import any other data from other sources. Just remember to keep in the Yolov7 Pytorch form accept
+
+![](https://raw.githubusercontent.com/Owaiskhan9654/Yolo-V7-Custom-Dataset-Train-on-Kaggle/main/Yolov7%20Pytorch%20format.JPG)
+
+```python
+user_secrets = UserSecretsClient()
+roboflow_api_key = user_secrets.get_secret("roboflow_api")
+```
+
+
+```python
+rf = Roboflow(api_key=roboflow_api_key)
+project = rf.workspace("owais-ahmad").project("custom-yolov7-on-kaggle-on-custom-dataset-rakiq")
+dataset = project.version(2).download("yolov7")
+```
+
+# Step 3: Training Custom pretrained YOLOv7 model
+
+Here, I am able to pass a number of arguments:
+- **img:** define input image size
+- **batch:** determine batch size
+- **epochs:** define the number of training epochs. (Note: often, 3000+ are common here nut since I am using free version of colab I will be only defining it to 20!)
+- **data:** Our dataset locaiton is saved in the `./yolov7/Custom-Yolov7-on-Kaggle-on-Custom-Dataset-2` folder.
+- **weights:** specifying a path to weights to start transfer learning from. Here I have choosen a generic COCO pretrained checkpoint.
+- **cache:** caching images for faster training
+
+
+```python
+!python train.py --batch 16 --cfg cfg/training/yolov7.yaml --epochs 30 --data {dataset.location}/data.yaml --weights 'yolov7.pt' --device 0 
+
+```
+
+# Run Inference  With Trained Weights
+Testing inference with a pretrained checkpoint on contents of `./Custom-Yolov7-on-Kaggle-on-Custom-Dataset-2/test/images` folder downloaded from Roboflow.
+
+
+```python
+!python detect.py --weights runs/train/exp/weights/best.pt --img 416 --conf 0.75 --source ./Custom-Yolov7-on-Kaggle-on-Custom-Dataset-2/test/images
+```
+
+# Display inference on ALL test images
+
+
+```python
+for images in glob.glob('runs/detect/exp/*.jpg')[0:10]:
+    display(Image(filename=images))
+```
+
+
+```python
+model = torch.load('runs/train/exp/weights/best.pt')
+```
+
+# Conclusion and Next Steps
+
+Now this trained custom YOLOv7 model can be used to recognize **Person** and **Cars** form any given Images.
+
+To improve the model's performance, I might perform more interating on the datasets coverage,propper annotations and and Image quality. From orignal authors of **Yolov7** this guide has been given for [model performance improvement](https://github.com/WongKinYiu/yolov7).
+
+To deploy our model to an application by [exporting your model to deployment destinations](https://github.com/WongKinYiu/yolov7/issues).
+
+Once our model is in production, I will be willing  to continually iterate and improve on your dataset and model via [active learning](https://blog.roboflow.com/what-is-active-learning/).