How can we fineturn the trained PatchCore model

[zhanghuayu-seu]

What is the motivation for this task?

We need to update the model in the system.

Describe the solution you'd like

I have trained the patchcore model using my dataset and save the weights in .ckpt and onnx files. Now we get a few images (around 50 images), how can we update the model based on the trained patchcore model in the small datasets. We are looking forward to your reply.

Additional context

dataset:
name: anomaly
format: folder
path: ./datasets/Anomaly
normal_dir: normal
abnormal_dir: anomaly
mask_dir: mask/anomaly
normal_test_dir: null
extensions: null
task: segmentation
category: bottle
train_batch_size: 1
eval_batch_size: 1
num_workers: 1
image_size: 256
center_crop: 224
normalization: imagenet
transform_config:
train: null
eval: null
test_split_mode: from_dir
test_split_ratio: 0.2
val_split_mode: same_as_test
val_split_ratio: 0.5
tiling:
apply: false
tile_size: null
stride: null
remove_border_count: 0
use_random_tiling: false
random_tile_count: 16
model:
name: patchcore
backbone: wide_resnet50_2
pre_trained: true
layers:

• layer2
• layer3
  coreset_sampling_ratio: 0.1
  num_neighbors: 1
  normalization_method: min_max
  metrics:
  image:
• F1Score
• AUROC
  pixel:
• F1Score
• AUROC
  threshold:
  method: adaptive
  manual_image: null
  manual_pixel: null
  visualization:
  show_images: false
  save_images: true
  log_images: true
  image_save_path: null
  mode: custom
  project:
  seed: 0
  path: ./results
  logging:
  logger: []
  log_graph: false
  optimization:
  export_mode: onnx
  trainer:
  enable_checkpointing: true
  default_root_dir: null
  gradient_clip_val: 0
  gradient_clip_algorithm: norm
  num_nodes: 1
  devices: 1
  enable_progress_bar: true
  overfit_batches: 0.0
  track_grad_norm: -1
  check_val_every_n_epoch: 1
  fast_dev_run: false
  accumulate_grad_batches: 1
  max_epochs: 1
  min_epochs: null
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  limit_predict_batches: 1.0
  val_check_interval: 1.0
  log_every_n_steps: 50
  accelerator: cpu
  strategy: null
  sync_batchnorm: false
  precision: 32
  enable_model_summary: true
  num_sanity_val_steps: 0
  profiler: null
  benchmark: false
  deterministic: false
  reload_dataloaders_every_n_epochs: 0
  auto_lr_find: false
  replace_sampler_ddp: true
  detect_anomaly: false
  auto_scale_batch_size: false
  plugins: null
  move_metrics_to_cpu: false
  multiple_trainloader_mode: max_size_cycle

[abc-125]

Hello, if I understood correctly, you need to use both your dataset and new images to train the new PatchCore model; you cannot finetune it. PatchCore is not being trained in the usual way; instead, it extracts features from the training dataset and then uses these features to determine how different the new input is. You can find more details here.

[zhanghuayu-seu]

Thank you for your reply. I want to know which models can support finetune, looking forward to your reply.

[abc-125]

You can try other models, which can be trained for longer than one epoch, such as EfficientAD or FastFlow. I have not tested it personally, so I would suggest comparing the results of a finetuned model and a model, trained on your dataset and these 50 new images altogether, to see if finetuning actually works.

[samet-akcay]

As @abc-125 mentioned, those images may not help for the training for the patchcore model. They could help for the validation when tuning your threshold value to get a better performance.