Assessing Foundation Models for Mold Colony Detection with Limited Training Data

• URL: http://arxiv.org/abs/2510.00561v1
• Date: Wed, 01 Oct 2025 06:25:45 GMT
• Title: Assessing Foundation Models for Mold Colony Detection with Limited Training Data
• Abstract summary: We build a representative dataset of 5000 Petri dish images annotated with bounding boxes.<n>We benchmark three vision foundation models against traditional baselines on task specific metrics.<n>MaskDINO attains near-parity with an extensively trained YoloV9 model while finetuned only on 150 images.
• Score: 13.811543290168684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The process of quantifying mold colonies on Petri dish samples is of critical importance for the assessment of indoor air quality, as high colony counts can indicate potential health risks and deficiencies in ventilation systems. Conventionally the automation of such a labor-intensive process, as well as other tasks in microbiology, relies on the manual annotation of large datasets and the subsequent extensive training of models like YoloV9. To demonstrate that exhaustive annotation is not a prerequisite anymore when tackling a new vision task, we compile a representative dataset of 5000 Petri dish images annotated with bounding boxes, simulating both a traditional data collection approach as well as few-shot and low-shot scenarios with well curated subsets with instance level masks. We benchmark three vision foundation models against traditional baselines on task specific metrics, reflecting realistic real-world requirements. Notably, MaskDINO attains near-parity with an extensively trained YoloV9 model while finetuned only on 150 images, retaining competitive performance with as few as 25 images, still being reliable on $\approx$ 70% of the samples. Our results show that data-efficient foundation models can match traditional approaches with only a fraction of the required data, enabling earlier development and faster iterative improvement of automated microbiological systems with a superior upper-bound performance than traditional models would achieve.

Related papers

• Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification [0.0]
  This thesis aims to train a new Base Model based on the DenseNet201 architecture to classify plant leaf disease.<n>Open datasets for the field of plant leaf disease classification have been identified as well as models that can be trained on them.
  arXiv  Detail & Related papers  (2026-05-02T06:33:38Z)
• Bridging Foundation Models and ASTM Metallurgical Standards for Automated Grain Size Estimation from Microscopy Images [4.3441332321802095]
  We propose an automated pipeline for dense segmentation and grain size estimation.<n>We benchmark this pipeline against a classical convolutional network (U-Net), an adaptive-prompting vision foundation model (MatSAM) and a contemporary vision-language model (Qwen2.5-VL-7B)<n>Experiments across progressively reduced training splits demonstrate exceptional few-shot scalability.
  arXiv  Detail & Related papers  (2026-04-21T01:13:03Z)
• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks [61.16389024252561]
  We develop a robust generalist perception model capable of addressing multiple tasks under constraints of computational resources and limited training data.<n>We leverage text-to-image diffusion models pre-trained on billions of images and successfully introduce our DICEPTION, a visual generalist model.<n> Exhaustive evaluations demonstrate that DICEPTION effectively tackles diverse perception tasks, even achieving performance comparable to SOTA single-task specialist models.
  arXiv  Detail & Related papers  (2025-02-24T13:51:06Z)
• HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing [21.192836739734435]
  On-board processing of hyperspectral data with machine learning models would enable unprecedented amount of autonomy for a wide range of tasks.<n>This can enable early warning system and could allow new capabilities such as automated scheduling across constellations of satellites.<n>We propose fast and accurate machine learning architectures which support end-to-end training with data of high spectral dimension.
  arXiv  Detail & Related papers  (2024-10-22T17:59:55Z)
• LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content [62.816876067499415]
  We propose LiveXiv: a scalable evolving live benchmark based on scientific ArXiv papers.<n>LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs.<n>We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities.
  arXiv  Detail & Related papers  (2024-10-14T17:51:23Z)
• No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance [68.18779562801762]
  multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance. Our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found.
  arXiv  Detail & Related papers  (2024-04-04T17:58:02Z)
• Predictive Analytics of Varieties of Potatoes [2.336821989135698]
  We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties.
  arXiv  Detail & Related papers  (2024-04-04T00:49:05Z)
• On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation [47.95611203419802]
  Foundations for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. We compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset. We further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution data.
  arXiv  Detail & Related papers  (2023-11-18T14:52:10Z)
• Few-shot learning approaches for classifying low resource domain specific software requirements [1.1470070927586016]
  Few-shot learning is a type of deep learning that uses only a few annotated samples. Our experiments focus on classifying BOSCH automotive domain textual software requirements into 3 categories. While SciBERT and DeBERTa based models tend to be the most accurate at 15 training samples, their performance improvement scales minimally as the number of annotated samples is increased to 50 in comparison to Siamese and T5 based models.
  arXiv  Detail & Related papers  (2023-02-14T10:19:23Z)
• Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling [8.329870357145927]
  Coherence evaluation of machine generated text is one of the principal applications of coherence models that needs to be investigated. We explore training data and self-supervision objectives that result in a model that generalizes well across tasks. We show empirically that increasing the density of negative samples improves the basic model, and using a global negative queue further improves and stabilizes the model while training with hard negative samples.
  arXiv  Detail & Related papers  (2021-10-14T07:44:14Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• CHEER: Rich Model Helps Poor Model via Knowledge Infusion [69.23072792708263]
  We develop a knowledge infusion framework named CHEER that can succinctly summarize such rich model into transferable representations. Our empirical results showed that CHEER outperformed baselines by 5.60% to 46.80% in terms of the macro-F1 score on multiple physiological datasets.
  arXiv  Detail & Related papers  (2020-05-21T21:44:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.