### Setting up YOLOv9 Docker Environment (Shell)

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

This script creates a Docker container using `nvidia-docker`, mounts local directories for COCO dataset and YOLOv9 code, sets the shared memory size, installs required system packages (`zip`, `htop`, `screen`, `libgl1-mesa-glx`) using `apt`, installs Python packages (`seaborn`, `thop`) using `pip`, and changes the current directory to the YOLOv9 code folder inside the container.

```shell
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov9 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov9 --shm-size=64g nvcr.io/nvidia/pytorch:21.11-py3

# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx

# pip install required packages
pip install seaborn thop

# go to code folder
cd /yolov9
```

--------------------------------

### Prepare COCO Dataset - Shell

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Shell script command to download and prepare the MS COCO dataset for training and validation. Requires bash execution.

```Shell
bash scripts/get_coco.sh
```

--------------------------------

### Infer with YOLOv9 Model - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to run inference on an image using the standard yolov9-c model via detect_dual.py. Specifies source image, image size, device, weights file, and output name.

```Python
python detect_dual.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c.pt' --name yolov9_c_640_detect
```

--------------------------------

### Infer with GELAN Model - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to run inference on an image using the gelan-c model. Specifies source image, image size, device, weights file, and output name.

```Python
python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './gelan-c.pt' --name gelan_c_c_640_detect
```

--------------------------------

### Infer with Converted YOLOv9 Model - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to run inference on an image using a converted yolov9-c model. Specifies source image, image size, device, weights file, and output name.

```Python
python detect.py --source './data/images/horses.jpg' --img 640 --device 0 --weights './yolov9-c-converted.pt' --name yolov9_c_c_640_detect
```

--------------------------------

### Train GELAN Model (Multi GPU) - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command for distributed multi-GPU training of the gelan-c model. Sets number of processes, master port, devices, sync batch normalization, batch size, data, image size, model config, weights (none), run name, hyperparameters, minimum items, epochs, and mosaic closing.

```Python
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
```

--------------------------------

### Train GELAN Model (Single GPU) - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command for single GPU training of the gelan-c model. Configures workers, device, batch size, data, image size, model config, weights (none), run name, hyperparameters, minimum items, epochs, and mosaic closing.

```Python
python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
```

--------------------------------

### Train YOLOv9 Model (Multi GPU) - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command for distributed multi-GPU training of the yolov9-c model using torch.distributed.launch. Specifies number of processes, master port, devices, sync batch normalization, batch size, data, image size, model config, weights (none), run name, hyperparameters, minimum items, epochs, and mosaic closing.

```Python
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_dual.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch 128 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
```

--------------------------------

### Train YOLOv9 Model (Single GPU) - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command for single GPU training of the yolov9-c model. Sets workers, device, batch size, data config, image size, model config, initial weights (none), run name, hyperparameters, minimum items per batch, epochs, and mosaic augmentation closing epoch.

```Python
python train_dual.py --workers 8 --device 0 --batch 16 --data data/coco.yaml --img 640 --cfg models/detect/yolov9-c.yaml --weights '' --name yolov9-c --hyp hyp.scratch-high.yaml --min-items 0 --epochs 500 --close-mosaic 15
```

--------------------------------

### Train GELAN-C for Instance Segmentation

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to train the GELAN-C model for instance segmentation using the COCO dataset. Includes parameters for workers, device, batch size, data, image size, config, weights, name, hyperparameters, and training specifics like epochs and mosaic. Requires instance labels in polygon format.

```shell
python segment/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/segment/gelan-c-seg.yaml --weights '' --name gelan-c-seg --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
```

--------------------------------

### Train GELAN-C for Object Detection (bbox)

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to train the GELAN-C model for object detection using the COCO dataset. Specifies hyperparameters, batch size, image size, configuration file, and output name. Requires the YOLOv9 repository, Python, COCO dataset, and specified config/hyp files.

```shell
python train.py --workers 8 --device 0 --batch 32 --data data/coco.yaml --img 640 --cfg models/detect/gelan-c.yaml --weights '' --name gelan-c-det --hyp hyp.scratch-high.yaml --min-items 0 --epochs 300 --close-mosaic 10
```

--------------------------------

### Validate GELAN Model - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to validate the gelan-c model on the COCO dataset using val.py. Configures image size, batch size, confidence and IoU thresholds, device, weights file, and saves results to JSON.

```Python
python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './gelan-c.pt' --save-json --name gelan_c_640_val
```

--------------------------------

### Evaluating Converted YOLOv9 Models (Shell)

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

This command executes the `val.py` script to evaluate a converted YOLOv9 model (`yolov9-c-converted.pt`) on the COCO dataset. It specifies the data configuration, image size, batch size, confidence threshold, IOU threshold, device (GPU 0), weights file, saves results in JSON format, and names the run.

```shell
# evaluate converted yolov9 models
python val.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c-converted.pt' --save-json --name yolov9_c_c_640_val
```

--------------------------------

### Train GELAN-C for Panoptic Segmentation

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to train the GELAN-C model for panoptic segmentation using the COCO dataset. Specifies parameters for workers, device, batch size, data, image size, config, weights, name, hyperparameters, and training specifics. Requires instance labels and semantic stuff labels in polygon format.

```shell
python panoptic/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/panoptic/gelan-c-pan.yaml --weights '' --name gelan-c-pan --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
```

--------------------------------

### Validate YOLOv9 Model - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to validate the yolov9-c model on the COCO dataset using val_dual.py. Specifies image size, batch size, confidence and IoU thresholds, device, weights file, and saves results to JSON.

```Python
python val_dual.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.7 --device 0 --weights './yolov9-c.pt' --save-json --name yolov9_c_640_val
```

--------------------------------

### Train GELAN-C for Image Captioning

Source: https://github.com/wongkinyiu/yolov9/blob/main/README.md

Command to train the GELAN-C model for image captioning using the COCO dataset. Details the required input formats for instance labels, stuff segmentation, and annotations (JSON). Specifies training parameters like workers, device, batch size, data, image size, config, weights, name, hyperparameters, and epochs.

```shell
python caption/train.py --workers 8 --device 0 --batch 32  --data coco.yaml --img 640 --cfg models/caption/gelan-c-cap.yaml --weights '' --name gelan-c-cap --hyp hyp.scratch-high.yaml --no-overlap --epochs 300 --close-mosaic 10
```

--------------------------------

### Initializing and Loading YOLOv9-S Model - PyTorch

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

Sets the device to CPU, defines the model configuration file path, initializes the YOLOv9 model using the configuration, loads pre-trained weights from a checkpoint file, and sets the model to evaluation mode. Requires the model configuration file and the pre-trained checkpoint file.

```python
device = torch.device("cpu")
cfg = "./models/detect/gelan-s.yaml"
model = Model(cfg, ch=3, nc=80, anchors=3)
#model = model.half()
model = model.to(device)
_ = model.eval()
ckpt = torch.load('./yolov9-s.pt', map_location='cpu')
model.names = ckpt['model'].names
model.nc = ckpt['model'].nc
```

--------------------------------

### Initializing YOLOv9-M Model and Loading Weights (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This snippet initializes a YOLOv9-M model from a configuration file (`gelan-m.yaml`) and loads pre-trained weights from a PyTorch checkpoint (`yolov9-m.pt`). It sets the device to CPU and prepares the model for evaluation by calling `.eval()`. It also copies the names and number of classes from the loaded checkpoint to the model object.

```python
device = torch.device("cpu")
cfg = "./models/detect/gelan-m.yaml"
model = Model(cfg, ch=3, nc=80, anchors=3)
#model = model.half()
model = model.to(device)
_ = model.eval()
ckpt = torch.load('./yolov9-m.pt', map_location='cpu')
model.names = ckpt['model'].names
model.nc = ckpt['model'].nc
```

--------------------------------

### Initializing YOLOv9-E Model and Loading Checkpoint (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This snippet initializes the YOLOv9-E model architecture using a configuration file and loads pre-trained weights from a PyTorch checkpoint file (`yolov9-e.pt`). It sets the device to CPU, defines the model structure, and loads the state dictionary, including names and class count.

```python
device = torch.device("cpu")
cfg = "./models/detect/gelan-e.yaml"
model = Model(cfg, ch=3, nc=80, anchors=3)
#model = model.half()
model = model.to(device)
_ = model.eval()
ckpt = torch.load('./yolov9-e.pt', map_location='cpu')
model.names = ckpt['model'].names
model.nc = ckpt['model'].nc
```

--------------------------------

### Initialize YOLOv9-C Model and Load Checkpoint (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This snippet initializes a YOLOv9-C model structure using a configuration file and loads a pre-trained PyTorch checkpoint onto it. It sets the device to CPU, loads the model state dictionary, and updates model attributes like names and class count from the checkpoint. The model is then set to evaluation mode.

```python
device = torch.device("cpu")
cfg = "./models/detect/gelan-c.yaml"
model = Model(cfg, ch=3, nc=80, anchors=3)
#model = model.half()
model = model.to(device)
_ = model.eval()
ckpt = torch.load('./yolov9-c.pt', map_location='cpu')
model.names = ckpt['model'].names
model.nc = ckpt['model'].nc
```

--------------------------------

### Saving Converted YOLOv9-S Model Checkpoint - PyTorch

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

Creates a dictionary containing the converted model's state dictionary (converted to half precision), along with placeholder values for optimizer, fitness, EMA, etc. This dictionary is then saved as a new PyTorch checkpoint file, ready for deployment or further use.

```python
m_ckpt = {'model': model.half(),
          'optimizer': None,
          'best_fitness': None,
          'ema': None,
          'updates': None,
          'opt': None,
          'git': None,
          'date': None,
          'epoch': -1}
torch.save(m_ckpt, "./yolov9-s-converted.pt")
```

--------------------------------

### Logging Dependencies - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/requirements.txt

Libraries used for logging and tracking experiment metrics and results, such as TensorBoard.

```Python
tensorboard>=2.4.1
```

--------------------------------

### Saving Converted YOLOv9-M Checkpoint (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This snippet constructs a dictionary (`m_ckpt`) containing the converted model's state dictionary (converted to half precision using `.half()`) and sets other standard checkpoint keys (like optimizer, epoch, etc.) to None or default values. Finally, it saves this dictionary to a new PyTorch checkpoint file named `yolov9-m-converted.pt`.

```python
m_ckpt = {'model': model.half(),
          'optimizer': None,
          'best_fitness': None,
          'ema': None,
          'updates': None,
          'opt': None,
          'git': None,
          'date': None,
          'epoch': -1}
torch.save(m_ckpt, "./yolov9-m-converted.pt")
```

--------------------------------

### Saving Converted YOLOv9-E Model Checkpoint (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This snippet constructs a new checkpoint dictionary containing the converted model's state dictionary (converted to half precision). It includes placeholder values for other checkpoint components like optimizer, fitness, etc., and saves the entire dictionary to a new PyTorch checkpoint file named `yolov9-e-converted.pt`.

```python
m_ckpt = {'model': model.half(),
          'optimizer': None,
          'best_fitness': None,
          'ema': None,
          'updates': None,
          'opt': None,
          'git': None,
          'date': None,
          'epoch': -1}
torch.save(m_ckpt, "./yolov9-e-converted.pt")
```

--------------------------------

### Saving Converted YOLOv9-C Model Checkpoint (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This snippet prepares a dictionary containing the converted YOLOv9-C model, converting it to half precision. It includes placeholder values for other standard checkpoint components like optimizer state and training metadata. Finally, it saves this dictionary to a new PyTorch checkpoint file named "yolov9-c-converted.pt".

```python
m_ckpt = {'model': model.half(),
          'optimizer': None,
          'best_fitness': None,
          'ema': None,
          'updates': None,
          'opt': None,
          'git': None,
          'date': None,
          'epoch': -1}
torch.save(m_ckpt, "./yolov9-c-converted.pt")
```

--------------------------------

### Transferring Weights to YOLOv9-E Model State Dictionary (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This code iterates through the state dictionary of the initialized model and the loaded checkpoint. It matches keys based on module index (`idx`) and applies specific mapping rules for different module types (e.g., `cv2` to `cv4`, `cv3` to `cv5`, `dfl` to `dfl2`) to transfer weights. It prints a message upon successful matching and transfer.

```python
idx = 0
for k, v in model.state_dict().items():
    if "model.{}.".format(idx) in k:
        if idx < 29:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif idx < 42:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
    else:
        while True:
            idx += 1
            if "model.{}.".format(idx) in k:
                break
        if idx < 29:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif idx < 42:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
_ = model.eval()
```

--------------------------------

### Transferring YOLOv9-M Weights (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This code iterates through the state dictionary of the initialized model and the loaded checkpoint. It attempts to match layer names based on an index (`idx`) and specific layer types (`cv2`, `cv3`, `dfl`). If a match is found, it copies the weights from the checkpoint to the model's state dictionary, effectively transferring the learned parameters. It includes print statements to confirm successful matches.

```python
idx = 0
for k, v in model.state_dict().items():
    if "model.{}.".format(idx) in k:
        if idx < 22:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+1))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
    else:
        while True:
            idx += 1
            if "model.{}.".format(idx) in k:
                break
        if idx < 22:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+1))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
_ = model.eval()
```

--------------------------------

### Importing Libraries and Model - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

Imports the necessary PyTorch library for tensor operations and the custom Model class from the local models.yolo module, which is required to define the YOLOv9 model architecture.

```python
import torch
from models.yolo import Model
```

--------------------------------

### Base Dependencies - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/requirements.txt

Core Python libraries required for the fundamental operation of the YOLOv9 project, including numerical computation, image processing, and deep learning frameworks.

```Python
gitpython
ipython
matplotlib>=3.2.2
numpy>=1.18.5
opencv-python>=4.1.1
Pillow>=7.1.2
psutil
PyYAML>=5.3.1
requests>=2.23.0
scipy>=1.4.1
thop>=0.1.1
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.64.0
```

--------------------------------

### Mapping Checkpoint Weights to Model State Dictionary - PyTorch

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

Iterates through the keys of the initialized model's state dictionary. It attempts to find corresponding keys in the loaded checkpoint's state dictionary, applying specific mapping rules based on layer indices and names (e.g., cv2 to cv4, cv3 to cv5, dfl to dfl2) and transferring the weights. This process is crucial for adapting pre-trained weights to a potentially modified model structure.

```python
idx = 0
for k, v in model.state_dict().items():
    if "model.{}.".format(idx) in k:
        if idx < 22:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
    else:
        while True:
            idx += 1
            if "model.{}.".format(idx) in k:
                break
        if idx < 22:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+7))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
            print(k, "perfectly matched!!")
_ = model.eval()
```

--------------------------------

### Plotting Dependencies - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/requirements.txt

Libraries for data visualization and plotting results, including pandas for data manipulation and seaborn for statistical graphics.

```Python
pandas>=1.1.4
seaborn>=0.11.0
```

--------------------------------

### Transferring Weights Between YOLOv9-C Model Layers (Python)

Source: https://github.com/wongkinyiu/yolov9/blob/main/tools/reparameterization.ipynb

This code iterates through the model's state dictionary to transfer weights from a source checkpoint to the target model structure. It identifies layers based on their index and name patterns (e.g., cv2, cv3, dfl) and copies the corresponding weights from the loaded checkpoint's state dictionary, effectively remapping or updating weights for specific parts of the model.

```python
idx = 0
for k, v in model.state_dict().items():
    if "model.{}.".format(idx) in k:
        if idx < 22:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+1))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
    else:
        while True:
            idx += 1
            if "model.{}.".format(idx) in k:
                break
        if idx < 22:
            kr = k.replace("model.{}.".format(idx), "model.{}.".format(idx+1))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
        elif "model.{}.cv2.".format(idx) in k:
            kr = k.replace("model.{}.cv2.".format(idx), "model.{}.cv4.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
        elif "model.{}.cv3.".format(idx) in k:
            kr = k.replace("model.{}.cv3.".format(idx), "model.{}.cv5.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
        elif "model.{}.dfl.".format(idx) in k:
            kr = k.replace("model.{}.dfl.".format(idx), "model.{}.dfl2.".format(idx+16))
            model.state_dict()[k] -= model.state_dict()[k]
            model.state_dict()[k] += ckpt['model'].state_dict()[kr]
_ = model.eval()
```

--------------------------------

### Extras Dependencies - Python

Source: https://github.com/wongkinyiu/yolov9/blob/main/requirements.txt

Additional dependencies that might be required for specific features or functionalities, such as data augmentation (albumentations) and COCO dataset evaluation (pycocotools).

```Python
albumentations>=1.0.3
pycocotools>=2.0
```

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