Continue training from old model with different number of classes

[jobjansweijer]

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Question

Hi there,

I previously trained a model on a dataset with ~600 classes. Now I have expanded my dataset to ~1100 classes, with similar images.

I would like to use the old .pt file as a base to continue training. However, the resume parameter does not seem to work in this case, because of the mismatch in number of classes.

I'm aware I'll need to train on my entire dataset and cannot incrementally add new classes.

Code

import torch
from ultralytics import YOLO

data_path = 'images/temp'
file = f'./runs/classify/1M_images/weights/last.pt'

# Check for CUDA device and set it
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Define model and load it to the specified device
model = YOLO(file).to(device)

# Train the model
results = model.train(
    data=data_path, 
    epochs=50, 
    name=filename, 
    resume=True 
)

Questions:

1) My assumption is that if I use my old model as a base, I will need to train fewer epochs to achieve good results. Is that assumption correct? 2) How can I continue training from my old model which has a different number of classes?

Additional

No response

[glenn-jocher]

Hello!

To address your issue of continuing training with a different number of classes:

1) Assumption on Fewer Epochs: Yes, generally when using a pre-trained model, the network may require fewer epochs to converge, especially if the new dataset is similar to the initial one.

2) Continuing Training Across Different Class Sizes: Unfortunately, you cannot directly resume training from a checkpoint with a different number of classes due to the mismatch in the final layer's dimensions. However, you can still leverage the pre-trained weights for other layers, which will help in faster and potentially better convergence for your new model.

You would need to adjust the architecture to accommodate the new class size and load the weights except for the final layer like this:

from ultralytics import YOLO, Model

# Load pre-trained model while ignoring the final layer
model = YOLO('./runs/classify/1M_images/weights/last.pt', ignore_last_layer=True)

# Add the new number of classes as follows
model.classes = 1100  # set your new number of classes

# Continue training the new model
results = model.train(
    data=data_path, 
    epochs=50, 
    name=filename
)

This way, you retain most of the learned features and only need to adjust the final classification layer to your new dataset.

Feel free to reach out if you need more guidance. Good luck with your training! 🚀

[jobjansweijer]

Thanks for the response, Glenn. I think something might be wrong with my implementation or with your example... When I try to import Model (or model), I'm getting:

    from ultralytics import YOLO, Model
ImportError: cannot import name 'Model' from 'ultralytics' (C:\Users\Gebruiker\miniconda3\Lib\site-packages\ultralytics\__init__.py)

When I leave out Model from the import line, I'm getting:

    model = YOLO(filepath, ignore_last_layer=True)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Model.__init__() got an unexpected keyword argument 'ignore_last_layer'

I've already updated Ultralytics using pip install -U ultralytics

What's going wrong?

[glenn-jocher]

Hello!

Thank you for reaching out with your issue. It seems like there might have been a bit of confusion with the example provided. My apologies for that. In the Ultralytics YOLO library, there is no Model class and the ignore_last_layer argument is not a direct feature supported in the YOLO() constructor.

To load a model while excluding the final layer responsible for class predictions, you would typically modify the model definition itself or manipulate the pre-trained model's weights such that the last layer (which differs in number of classes) is reinitialized.

Here's an adjusted example using just the classes from the YOLO object that should work with the current library capabilities:

from ultralytics import YOLO

# Load your pre-trained model
model = YOLO('path_to_your_model/last.pt')

# Assuming you need to adjust the output layer to fit a different number of classes:
model.model[-1] = [YOUR_NEW_OUTPUT_LAYER_SPECIFICATION]  # Modify according to the new class size specifications

# Continue training the modified model
results = model.train(
    data='your_data_path', 
    epochs=50
)

Please replace [YOUR_NEW_OUTPUT_LAYER_SPECIFICATION] with the actual layer specification needed for your new class size. You may need to peek into model.model to understand how the layers are structured and specifically target the layer(s) that correspond to class predictions.

Let me know if you need further assistance!

[jobjansweijer]

Hi Glenn,

Thanks for your response. Unfortunately I do not yet understand what to put at YOUR_NEW_OUTPUT_LAYER_SPECIFICATION. I'm still in the process of learning :-)

I've tried looking into model.py (is that what you mean with peeking into model.model?). But I don't see anything relevant about layers there... I did fid the yolov8-cls.yaml file pasted below, but when I try something like model.model[-1] = [-1, 1, 'Classify', [1100]] (which was just a guess), I'm getting TypeError: 'ClassificationModel' object does not support item assignment

Could you clarify what to put at YOUR_NEW_OUTPUT_LAYER_SPECIFICATION if I want to train a model with 1100 classes, based on my previous model which had 600 classes and was based on yolov8n-cls.pt?

# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify

# Parameters
nc: 1000 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]
  s: [0.33, 0.50, 1024]
  m: [0.67, 0.75, 1024]
  l: [1.00, 1.00, 1024]
  x: [1.00, 1.25, 1024]

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 3, C2f, [1024, True]]

# YOLOv8.0n head
head:
  - [-1, 1, Classify, [nc]] # Classify

[glenn-jocher]

@jobjansweijer hi there!

It seems like there was a bit of confusion regarding adjusting the layer for changing the number of classes. My apologies for the unclear instructions earlier. 🙏

Unfortunately, updating the last layer of the model as suggested previously (model.model[-1] = ...) won't work straightforwardly due to the object's structure. Instead, you need to adjust the nc parameter in the YOLO architecture configuration file before training the model.

If you're looking to train a model with 1100 classes based on your previous configuration, here’s what you need to do:

• Modify the .yaml file that specifies the model architecture. In this file, change nc: 1000 to nc: 1100.

After modifying the .yaml file, you can initialize and train your model as follows:

from ultralytics import YOLO

# Load your modified model configuration
model = YOLO('path_to_modified/yolov8n-cls.yaml')

# Start training
results = model.train('your_dataset_yaml_file_here', epochs=50)

This approach allows the model to properly initialize with the correct number of output classes. Let me know if this helps or if you encounter any more issues!

Happy coding! 😊

[jobjansweijer]

I don't think that would work, right? The .yaml file I shared earlier is not the .yaml that relates to my pre-trained model. I shared it because I though that .yaml described the layers of the yolov8n-cls model (which was initially used) and might give pointers on what to fill in at model.model[-1] = ... Also, there is no reference to the location of my pretrained base.pt file in that .yaml.

The .yaml in the folder of my base train model also has a different structure. There is no nc parameter for number of classes in there. I don't have any other .yaml files.

mode: train
model: runs\classify\1M_images\weights\last.pt
data: images/temp
epochs: 50
time: null
patience: 50
batch: 16
imgsz: 224
save: true
save_period: -1
cache: false
device: cpu
workers: 0
project: null
name: 1M_images_33_s66_v66
exist_ok: false
pretrained: true
optimizer: auto
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
save_hybrid: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: false
opset: null
workspace: 4
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.0
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
label_smoothing: 0.0
nbs: 64
hsv_h: 0.33
hsv_s: 0.66
hsv_v: 0.66
degrees: 180
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.5
fliplr: 0.5
mosaic: 1.0
mixup: 0.0
copy_paste: 0.0
auto_augment: null
erasing: 0.0
crop_fraction: 1
cfg: null
tracker: botsort.yaml
save_dir: runs\classify\1M_images

[hannaliavoshka]

@jobjansweijer, Have you considered starting a new training using your previous pre-trained model as initial weights? In this case, use resume=False. If your expanded dataset includes previous ~600 classes within this ~1100 and the new images are similar to the old ones, the new model will benefit from starting training from the previous trained weights and should not forget the majority of information received from the first training (this is an assumption, not the fact, but it worth a try).

[jurijsnazarovsambientai]

@glenn-jocher are you running GPT to give those answers? Look very much like it based on patterns of replies.

[glenn-jocher]

@jurijsnazarovsambientai hello!

Haha, no GPT here, just aiming to provide detailed and helpful responses. If you have any questions or need clarification on anything, feel free to ask. Happy coding! 😊