Towards Free Data Selection with General-Purpose Models
Authors: Yichen Xie, Mingyu Ding, Masayoshi TOMIZUKA, Wei Zhan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify the effectiveness of Free Sel on various computer vision tasks. |
| Researcher Affiliation | Academia | Yichen Xie, Mingyu Ding , Masayoshi Tomizuka, Wei Zhan UC Berkeley {yichen_xie, myding, tomizuka, wzhan}@berkeley.edu |
| Pseudocode | Yes | Algorithm 1: Semantic Pattern Extraction |
| Open Source Code | Yes | Our code is available at https://github.com/yichen928/Free Sel. |
| Open Datasets | Yes | We carry out experiments on PASCAL VOC [14]. In line with prior work [1, 57], we combine the training and validation sets of PASCAL VOC 2007 and 2012 as the training data pool with 16, 551 images. |
| Dataset Splits | No | In line with prior work [1, 57], we combine the training and validation sets of PASCAL VOC 2007 and 2012 as the training data pool with 16, 551 images. The paper combines training and validation sets into a single training pool, but does not explicitly describe separate validation splits for model training or reproduction. |
| Hardware Specification | Yes | The time is estimated on a single NVIDIA TITAN RTX GPU. |
| Software Dependencies | No | The model is implemented based on mmdetection. We follow [57, 1] to train the model for 300 epochs with batch size 32 using SGD optimizer (momentum 0.9). No specific version numbers for mmdetection, PyTorch, or other libraries are provided. |
| Experiment Setup | Yes | The model is trained for 300 epochs with batch size 32 using SGD optimizer (momentum 0.9). The initial learning rate is 0.001, which decays to 0.0001 after 240 epochs. |