YOLOv8-16bit

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📚 This guide explains how to freeze YOLOv5 🚀 layers when transfer learning. Transfer learning is a useful way to quickly retrain a model on new data without having to retrain the entire network. Instead, part of the initial weights are frozen in place, and the rest of the weights are used to compute loss and are updated by the optimizer. This requires less resources than normal training and allows for faster training times, though it may also result in reductions to final trained accuracy.
UPDATED 25 September 2022.

Before You Start

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7. Models and datasets download automatically from the latest YOLOv5 release.

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Freeze Backbone

All layers that match the train.py freeze list in train.py will be frozen by setting their gradients to zero before training starts.

 # Freeze 
 freeze = [f'model.{x}.' for x in range(freeze)]  # layers to freeze 
 for k, v in model.named_parameters(): 
     v.requires_grad = True  # train all layers 
     if any(x in k for x in freeze): 
         print(f'freezing {k}') 
         v.requires_grad = False

To see a list of module names:

for k, v in model.named_parameters():
    print(k)

# Output
model.0.conv.conv.weight
model.0.conv.bn.weight
model.0.conv.bn.bias
model.1.conv.weight
model.1.bn.weight
model.1.bn.bias
model.2.cv1.conv.weight
model.2.cv1.bn.weight
...
model.23.m.0.cv2.bn.weight
model.23.m.0.cv2.bn.bias
model.24.m.0.weight
model.24.m.0.bias
model.24.m.1.weight
model.24.m.1.bias
model.24.m.2.weight
model.24.m.2.bias

Looking at the model architecture we can see that the model backbone is layers 0-9:

# YOLOv5 backbone 
 backbone: 
   # [from, number, module, args] 
   [[-1, 1, Focus, [64, 3]],  # 0-P1/2 
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4 
    [-1, 3, BottleneckCSP, [128]], 
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8 
    [-1, 9, BottleneckCSP, [256]], 
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16 
    [-1, 9, BottleneckCSP, [512]], 
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32 
    [-1, 1, SPP, [1024, [5, 9, 13]]], 
    [-1, 3, BottleneckCSP, [1024, False]],  # 9 
   ] 
  
 # YOLOv5 head 
 head: 
   [[-1, 1, Conv, [512, 1, 1]], 
    [-1, 1, nn.Upsample, [None, 2, 'nearest']], 
    [[-1, 6], 1, Concat, [1]],  # cat backbone P4 
    [-1, 3, BottleneckCSP, [512, False]],  # 13 
  
    [-1, 1, Conv, [256, 1, 1]], 
    [-1, 1, nn.Upsample, [None, 2, 'nearest']], 
    [[-1, 4], 1, Concat, [1]],  # cat backbone P3 
    [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small) 
  
    [-1, 1, Conv, [256, 3, 2]], 
    [[-1, 14], 1, Concat, [1]],  # cat head P4 
    [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium) 
  
    [-1, 1, Conv, [512, 3, 2]], 
    [[-1, 10], 1, Concat, [1]],  # cat head P5 
    [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large) 
  
    [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5) 
   ]

so we can define the freeze list to contain all modules with 'model.0.' - 'model.9.' in their names:

python train.py --freeze 10

Freeze All Layers

To freeze the full model except for the final output convolution layers in Detect(), we set freeze list to contain all modules with 'model.0.' - 'model.23.' in their names:

python train.py --freeze 24

Results

We train YOLOv5m on VOC on both of the above scenarios, along with a default model (no freezing), starting from the official COCO pretrained --weights yolov5m.pt:

train.py --batch 48 --weights yolov5m.pt --data voc.yaml --epochs 50 --cache --img 512 --hyp hyp.finetune.yaml

Accuracy Comparison

The results show that freezing speeds up training, but reduces final accuracy slightly.

GPU Utilization Comparison

Interestingly, the more modules are frozen the less GPU memory is required to train, and the lower GPU utilization. This indicates that larger models, or models trained at larger --image-size may benefit from freezing in order to train faster.

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Notebooks with free GPU:
Google Cloud Deep Learning VM. See GCP Quickstart Guide
Amazon Deep Learning AMI. See AWS Quickstart Guide
Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

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