---
datasets:
- imagenet-1k
metrics:
- accuracy
library_name: timm
---

# Model Card for Model ID

Based on **quasi-linear hyperbolic systems of PDEs** [[Liu et al, 2023](https://github.com/liuyao12/ConvNets-PDE-perspective)], the QLNet enters an uncharted water of ConvNet model space marked by the use of (element-wise) multiplication instead 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 study (hyper-paremeter tuning, optimizer, etc.) by the community.


![](https://huggingface.co/liuyao/QLNet/resolve/main/QLNet.jpeg)

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 demon that makes a particular transformation on the weights while leaving the output unchanged.

*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).*


## Model Details

### Model Description

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.



- **Developed by:** Yao Liu 刘杳
- **Model type:** Convolutional Neural Network (ConvNet)
- **License:** [More Information Needed]
- **Finetuned from model:** N/A (*trained from scratch*)

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [ConvNet from the PDE perspective](https://github.com/liuyao12/ConvNets-PDE-perspective)
- **Paper:** [A Novel ConvNet Architecture with a Continuous Symmetry](https://arxiv.org/abs/2308.01621)
- **Demo [optional]:** [More Information Needed]

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training and Testing Data

ImageNet-1k

[More Information Needed]

### Training Procedure 

We use the training script in `timm`

```
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 
```

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

### Results

top1 acc = 78.40

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

single GPU :(

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]