FOCUS: Familiar Objects in Common and Uncommon Settings
Authors: Priyatham Kattakinda, Soheil Feizi
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a detailed analysis of the performance of various popular image classifiers on our dataset and demonstrate a clear drop in accuracy when classifying images in uncommon settings. We also show that finetuning a model on our dataset drastically improves its ability to focus on the object of interest leading to better generalization. |
| Researcher Affiliation | Academia | 1University of Maryland, College Park, MD, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our dataset and code for evaluating models on FOCUS are available at https://github.com/priyathamkat/focus. |
| Open Datasets | Yes | Our dataset and code for evaluating models on FOCUS are available at https://github.com/priyathamkat/focus. |
| Dataset Splits | No | We start by randomly splitting the dataset into train and test sets, which are 70% and 30% of the dataset in size, respectively. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as particular GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like PyTorch, EfficientNet-PyTorch, CLIP, and timm, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We use SGD with a learning rate of 1e-4 to update the last layer (fully-connected layer) of each model for 10 epochs of the train split. |