Comments (3)
Active Learning
Active learning aims to facilitate the annotation process by letting the model choose candidates for new annotations. After a phase of training, the model runs inference on a stack of unlabeled images. We then calculate entropy mesure on the predicted sample and use samples with low entropy (or uncertainty) as new candidate for annotations. The annotator is put to contribution in order to accept/reject the proposed annotations. The accepted annotations are then incorporated into the training dataset and we start a new iteration of training.
How the annotations will be accepted/rejected is still to be determined...
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Test the Intel Python distribution
Intel claims their Python distro is much faster for anything numeric. Since it can be installed with conda, can we give it a try ?
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Federated learning
"In a typical machine learning system, an optimization algorithm like Stochastic Gradient Descent (SGD) runs on a large dataset partitioned homogeneously across servers in the cloud. Such highly iterative algorithms require low-latency, high-throughput connections to the training data. But in the Federated Learning setting, the data is distributed across millions of devices in a highly uneven fashion. In addition, these devices have significantly higher-latency, lower-throughput connections and are only intermittently available for training."
https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
Could this be applied in a later stage, e.g. to optimize our model through time ?
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Related Issues (20)
- Resolve naming convention for duplicate image filename
- Add configurable parent directory for patches
- BUG: GDL cannot write TIFFs larger than 4 GB
- BUG: broken CI pipeline HOT 1
- FEATURE: low contrast check for images
- BUG: broken docker image potentially due to memory leak
- BUG: ValueError: can't extend empty axis 0 using modes other than 'constant' or 'empty' HOT 1
- FEATURE: read only valid portion of imagery HOT 1
- BUG: AOIs out-of-memory error
- BUG: Docker image creation fails due to missing "libarchive" HOT 1
- FEATURE: add HRNet + OCR model architecture
- "[BUG]: aoi.raster called before assignment in tilling_segmentation.py, on error logging."
- DOCS: add documentation for verify script HOT 1
- BUG: Version Issue HOT 3
- FEATURE: refactor SegmentationDataset class
- Implement a base class for scripting models
- BUG: importing torch raises ImportError
- BUG: scale to minmax range
- Feature: geo-inference integration
- "[BUG]: NaN metric values"
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