Deep Isometric Learning for Visual Recognition
Authors: Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, Jitendra Malik
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experiments with so designed simple networks on image classification using the Image Net dataset (Deng et al., 2009; Russakovsky et al., 2015). Our results show that an ISONet with more than 100 layers is trainable and can achieve surprisingly competitive performance. We evaluate the performance of R-ISONet for object detection and instance segmentation on the COCO dataset (Lin et al., 2014). 3. Experiments |
| Researcher Affiliation | Academia | Haozhi Qi 1 Chong You 1 Xiaolong Wang 1 2 Yi Ma 1 Jitendra Malik 1 1UC Berkeley 2UC San Diego. |
| Pseudocode | No | The paper describes methods and architectures but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Haozhi Qi/ISONet. |
| Open Datasets | Yes | We test the performance of ISONets on the ILSVRC2012 image classification dataset (Deng et al., 2009; Russakovsky et al., 2015). |
| Dataset Splits | Yes | The training set contains 1.28 million images from 1,000 categories. Evaluation is performed on the validation set which contains 50,000 images. |
| Hardware Specification | No | The paper mentions 'The training is performed on 8 GPUs', but does not provide specific hardware details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., PyTorch 1.9, Python 3.8, CUDA 11.1). |
| Experiment Setup | Yes | All models are trained using SGD with weight decay 0.0001, momentum 0.9 and mini-batch size 256. The initial learning rate is 0.1 for R-ISONet and 0.02 for ISONet. The models are trained for 100 epochs with the learning rate subsequently divided by a factor of 10 at the 30th, 60th and 90th epochs. To stabilize training in the early stage, we perform linear scheduled warmup (Goyal et al., 2017) for the first 5 epochs for both our methods and baseline methods. The orthogonal regularization coefficient γ in (13) (when used) is 0.0001 except for ISONet-101 where a stronger regularization (0.0003) is needed. |