Update README.md

README.md

@@ -10,9 +10,9 @@ library_name: timm
 
 # Model Card for Model ID
 
-Based on quaslinear hyperbolic systems of PDEs, the QLNet explores a new model space for ConvNets that uses multiplication (of same-sized tensors) instead of ReLU as the nonlinearity. It achieves comparable accuracy as ResNet50 on ImageNet-1k, demonstrating that it has the same level of capacity/expressivity, and deserves more study (hyper-paremeter tuning) that I alone am not able to do.
+Based on quasi-linear hyperbolic systems of PDEs, the QLNet enters an uncharted water of the model space for ConvNets that uses multiplication (of same-sized tensors) instead of ReLU as the nonlinearity. It achieves comparable accuracy as ResNet50 on ImageNet-1k, demonstrating that it has the same level of capacity/expressivity, and deserves more study (hyper-paremeter tuning, optimizer, etc.) that I alone am not able to do.
 
-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).
+*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).*
 
 
 
@@ -20,17 +20,14 @@ This modelcard aims to be a base template for new models. It has been generated
 
 ### Model Description
 
-Instead of the `bottleneck` block of ResNet50 which consists of 1x1, 3x3, 1x1 in succession, we instead do a 1x1, split into two equal halves and **multiply** them, then apply a 3x3 (depthwise), and a 1x1, all *without* activation functions except at the end of the block, where we apply a *radial activation function* that I call `hardball`.
+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
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Model type:** Convolutiona Neural Network (ConvNet)
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** *from scratch*
 
 ### Model Sources [optional]
 
@@ -40,40 +37,6 @@ Instead of the `bottleneck` block of ResNet50 which consists of 1x1, 3x3, 1x1 in
 - **Paper [optional]:** [A Novel ConvNet Architecture with a Continuous Symmetry](https://arxiv.org/abs/2308.01621)
 - **Demo [optional]:** [More Information Needed]
 
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
@@ -82,15 +45,19 @@ Use the code below to get started with the model.
 
 ## Training Details
 
-### Training Data
+### Training and Testing Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+ImageNet-1k
 
 [More Information Needed]
 
 ### Training Procedure 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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]
 
@@ -105,35 +72,9 @@ Use the code below to get started with the model.
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
 ### Results
 
-[More Information Needed]
+top1 acc = 78.40
 
 #### Summary
 
@@ -145,18 +86,6 @@ Use the code below to get started with the model.
 
 [More Information Needed]
 
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
@@ -169,7 +98,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
-[More Information Needed]
+single GPU :(
 
 #### Software