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BloodCancerDetection-YOLO

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# Blood Cancer Detection with YOLOV5

# Intrudiction

This notes that I took for target detection with yolov5. In this project, blood cancer cells were detected.

<img src="https://github.com/safakgunes/Blood-Cancer-Detection-YOLOV5/blob/main/yolov5/runs/train/exp/val_batch1_pred.jpg?raw=true" alt="prediction" width="600"/>

## Create an environment

* Update pip with the following command.
```bash
$ python -m pip install --upgrade pip
```
* Create an enviroment.
```bash 
$ conda create --name yolov5 
```
* Activate this environment
```bash
$ conda activate yolov5
```
## Requirements

* Open the yolov5 directory
```bash
$ cd <yolov5_dir>
```
* Setup requirements
```bash
 $ pip install -r requirements.txt
```
* Make sure you have cuda and cudnn installs

## Train

* Make sure environment activated. Go to yolov5 directory.
```bash
$ cd <yolov5_dir>
```

* Run commands below
```bash
% python train.py --img 416 --data ../data.yaml --cfg ./models/yolov5s.yaml  --batch 32 --epochs 50
```
* You can see the training results and  weights, enter the directory below in the yolov5 folder.

```bash
$ cd <yolov5_dir>/runs/train
```
## Detect

* Run commands below

```bash
$ python detect.py --source ../dataset/test/images/ --weights ./runs/train/exp/weights/best.pt --conf 0.4
```                                 
*  You can see the detection results, enter the directory below in the yolov5 folder.     

```bash
$ cd <yolov5_dir>/runs/detect
```

	a/README.md		b/README.md
1	# Blood Cancer Detection with YOLOV5	1	# Blood Cancer Detection with YOLOV5
2		2
3	# Intrudiction	3	# Intrudiction
4		4
5	This notes that I took for target detection with yolov5. In this project, blood cancer cells were detected.	5	This notes that I took for target detection with yolov5. In this project, blood cancer cells were detected.
6		6
7	<img src="https://github.com/safakgunes/Blood-Cancer-Detection-YOLOV5/blob/main/yolov5/runs/train/exp/val_batch1_pred.jpg?raw=true" alt="prediction" width="600"/>	7	<img src="https://github.com/safakgunes/Blood-Cancer-Detection-YOLOV5/blob/main/yolov5/runs/train/exp/val_batch1_pred.jpg?raw=true" alt="prediction" width="600"/>
8		8
9	## Create an environment	9	## Create an environment
10		10
11	* Update pip with the following command.	11	* Update pip with the following command.
12	```bash	12	```bash
13	$ python -m pip install --upgrade pip	13	$ python -m pip install --upgrade pip
14	```	14	```
15	* Create an enviroment.	15	* Create an enviroment.
16	```bash	16	```bash
17	$ conda create --name yolov5	17	$ conda create --name yolov5
18	```	18	```
19	* Activate this environment	19	* Activate this environment
20	```bash	20	```bash
21	$ conda activate yolov5	21	$ conda activate yolov5
22	```	22	```
23	## Requirements	23	## Requirements
24		24
25	* Open the yolov5 directory	25	* Open the yolov5 directory
26	```bash	26	```bash
27	$ cd <yolov5_dir>	27	$ cd <yolov5_dir>
28	```	28	```
29	* Setup requirements	29	* Setup requirements
30	```bash	30	```bash
31	$ pip install -r requirements.txt	31	$ pip install -r requirements.txt
32	```	32	```
33	* Make sure you have cuda and cudnn installs	33	* Make sure you have cuda and cudnn installs
34		34
35	## Train	35	## Train
36		36
37	* Make sure environment activated. Go to yolov5 directory.	37	* Make sure environment activated. Go to yolov5 directory.
38	```bash	38	```bash
39	$ cd <yolov5_dir>	39	$ cd <yolov5_dir>
40	```	40	```
41		41
42	* Run commands below	42	* Run commands below
43	```bash	43	```bash
44	% python train.py --img 416 --data ../data.yaml --cfg ./models/yolov5s.yaml --batch 32 --epochs 50	44	% python train.py --img 416 --data ../data.yaml --cfg ./models/yolov5s.yaml --batch 32 --epochs 50
45	```	45	```
46	* You can see the training results and weights, enter the directory below in the yolov5 folder.	46	* You can see the training results and weights, enter the directory below in the yolov5 folder.
47		47
48	```bash	48	```bash
49	$ cd <yolov5_dir>/runs/train	49	$ cd <yolov5_dir>/runs/train
50	```	50	```
51	## Detect	51	## Detect
52		52
53	* Run commands below	53	* Run commands below
54		54
55	```bash	55	```bash
56	$ python detect.py --source ../dataset/test/images/ --weights ./runs/train/exp/weights/best.pt --conf 0.4	56	$ python detect.py --source ../dataset/test/images/ --weights ./runs/train/exp/weights/best.pt --conf 0.4
57	```	57	```
58	* You can see the detection results, enter the directory below in the yolov5 folder.	58	* You can see the detection results, enter the directory below in the yolov5 folder.
59		59
60	```bash	60	```bash
61	$ cd <yolov5_dir>/runs/detect	61	$ cd <yolov5_dir>/runs/detect
62	```	62	```