Small Defects detection using efficient_ad #2037
Comments
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Hi, such small defect are quite a challenging problem. We have a system in development for this problem (#1226), but even with that it's still quite challenging if they are really small. Can the defect appear over the entire image? If it's only limited to specific region, I'd use cropping to reduce the image size. |
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Hi, Thank you for answering. and also can you provide an example to make it work? ill share the results. |
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About cropping: my idea was that you should crop the image to only fit the area of interest as much as possible to avoid large files. But if the area fits the image well already, then this is not relevant. Tiling could work here, the tiled ensemble was specifically designed for such cases, but given the scale of your anomalies (dist particle on a paper) it'll still be quite challenging. |
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@shrinand1996 Can you share an example image of the anomaly you are trying to detect? |
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@TurboJonte Sure. This is a single image. multiply it by 4 to get the full image. As i was unable to load such a big file, icropped a part of it. To look at the image, youll have to download it and then zoom (really zoom in) to see some dirt black spots. There are multiple dust spots over the image. but this is the minimum size i need to detect. * defect in red circle |
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I see the defect. It is really small so I think that without the tiled ensemble this will probably not work. Defects would probably disappear with downscaling, but keeping a full image would cause memory problems. Check the PR #1226 for tiled ensemble, but it's not updated for v1. |
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@blaz-r thanks for your swift response. I will give it a try. can you provide a tutorial for the same? like a notebook or something? Thanks |
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There isn't a notebook as of yet, but the fork also has docs l, but you'll need to check them manually. You'd need to use the training script inside the tools folder. |
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Closing this as it seems to be duplicate of #1727. You could follow the updates from this issue or from the PR itself. |
I have a huge image which is obtained by horizontally adding feed from 4 5MP cameras. The defect in a single image itself is very small (4-5 pixels). I want to detect the same on the final image which is 4 times the size of the single image.
Currently, I am unable to detect these small defects on a single image also.
It will be helpful if some guidance can be provided to detect the same.
I am running the model with this configuration. (I have removed tiling as it didnt give better results). I have also tried with medium model with no luck.
Things I can do:
dataset:
name: testing
format: folder
path: testing
normal_dir: good
abnormal_dir: bad
normal_test_dir: good
mask: null
extensions: null
task: classification
train_batch_size: 1
eval_batch_size: 16
num_workers: 20
image_size: 512 # dimensions to which images are resized (mandatory)
center_crop: null # dimensions to which images are center-cropped after resizing (optional)
normalization: none # data distribution to which the images will be normalized: [none, imagenet]
transform_config:
train: null
eval: null
test_split_mode: from_dir # options: [from_dir, synthetic]
test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode)
val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic]
val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
model:
name: efficient_ad
teacher_out_channels: 384
model_size: small # options: [small, medium]
lr: 0.0001
weight_decay: 0.00001
padding: false
pad_maps: true # relevant for "padding: false", see EfficientAd in lightning_model.py
generic params
normalization_method: min_max # options: [null, min_max, cdf]
metrics:
image:
- F1Score
- AUROC
pixel:
- F1Score
- AUROC
threshold:
method: adaptive #options: [adaptive, manual]
manual_image: null
manual_pixel: null
visualization:
show_images: False # show images on the screen
save_images: True # save images to the file system
log_images: False # log images to the available loggers (if any)
image_save_path: null # path to which images will be saved
mode: full # options: ["full", "simple"]
project:
seed: 42
path: ./results
logging:
logger: [] # options: [comet, tensorboard, wandb, csv] or combinations.
log_graph: false # Logs the model graph to respective logger.
optimization:
export_mode: torch # options: torch, onnx, openvino
PL Trainer Args. Don't add extra parameter here.
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: 50
fast_dev_run: false
accumulate_grad_batches: 1
max_epochs: 200
min_epochs: null
max_steps: 70000
min_steps: null
max_time: null
limit_train_batches: 16.0
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: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
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: true
auto_scale_batch_size: false
plugins: null
move_metrics_to_cpu: false
multiple_trainloader_mode: max_size_cycle
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