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--- |
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datasets: |
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- imagenet-1k |
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metrics: |
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- accuracy |
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library_name: timm |
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license: apache-2.0 |
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pipeline_tag: image-classification |
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--- |
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# Model Card for Model ID |
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Based on **quasi-linear hyperbolic systems of PDEs** [[Liu et al, 2023](https://github.com/liuyao12/ConvNets-PDE-perspective)], the QLNet makes an entry into uncharted waters of ConvNet model space marked by the use of (element-wise) multiplication in lieu of ReLU as the primary nonlinearity. It achieves comparable performance as ResNet50 on ImageNet-1k (acc=**78.4**), demonstrating that it has the same level of capacity/expressivity, and deserves more analysis and study (hyper-paremeter tuning, optimizer, etc.) by the academic community. |
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One notable feature is that the architecture (trained or not) admits a *continuous* symmetry in its parameters. Check out the [notebook](https://colab.research.google.com/#fileId=https://huggingface.co/liuyao/QLNet/blob/main/QLNet_symmetry.ipynb) for a demo that makes a particular transformation on the weights while leaving the output *unchanged*. |
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*This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).* |
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## Model Details |
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### Model Description |
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Instead of the `bottleneck` block of ResNet50 which consists of 1x1, 3x3, 1x1 in succession, this simplest version of QLNet does a 1x1, splits into two equal halves and **multiplies** them, then applies a 3x3 (depthwise), and a 1x1, all *without* activation functions except at the end of the block, where a *radial activation function* that we call `hardball` is applied. |
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- **Developed by:** Yao Liu 刘杳 |
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- **Model type:** Convolutional Neural Network (ConvNet) |
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- **License:** [More Information Needed] |
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- **Finetuned from model:** N/A (*trained from scratch*) |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [ConvNet from the PDE perspective](https://github.com/liuyao12/ConvNets-PDE-perspective) |
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- **Paper:** [A Novel ConvNet Architecture with a Continuous Symmetry](https://arxiv.org/abs/2308.01621) |
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- **Demo:** [More Information Needed] |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch, timm |
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from qlnet import QLNet |
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model = QLNet() |
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model.load_state_dict(torch.load('qlnet-50-v0.pth.tar')['state_dict']) |
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model.eval() |
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``` |
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## Training Details |
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### Training and Testing Data |
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ImageNet-1k |
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[More Information Needed] |
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### Training Procedure |
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We use the training script in `timm` |
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``` |
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python3 train.py ../datasets/imagenet/ --model resnet50 --num-classes 1000 --lr 0.1 --warmup-epochs 5 --epochs 240 --weight-decay 1e-4 --sched cosine --reprob 0.4 --recount 3 --remode pixel --aa rand-m7-mstd0.5-inc1 -b 192 -j 6 --amp --dist-bn reduce |
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``` |
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### Results |
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qlnet-50-v0: acc=78.40 |
