Update README.md

README.md

@@ -1,9 +1,139 @@
 ---
-tags:
-- pytorch_model_hub_mixin
-- model_hub_mixin
+license: mit
+datasets:
+- imagenet1k
+metrics:
+- accuracy
 ---
+# VGG-like Kolmogorov-Arnold Convolutional network with Gram polynomials
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+This model is a Convolutional version of Kolmogorov-Arnold Network with VGG-11 like architecture, pretrained on Imagenet1k dataset. KANs were originally presented in [1, 2]. Gram version of KAN originally presented in [3]. For more details visit our [torch-conv-kan](https://github.com/IvanDrokin/torch-conv-kan) repository on GitHub.
+
+## Model description
+
+The model consists of consecutive 10 Gram ConvKAN Layers with InstanceNorm2d, polynomial degree equal to 5, GlobalAveragePooling and Linear classification head:
+
+1. KAGN Convolution, 32 filters, 3x3
+2. Max pooling, 2x2
+3. KAGN Convolution, 64 filters, 3x3
+4. Max pooling, 2x2
+5. KAGN Convolution, 128 filters, 3x3
+6. KAGN Convolution, 128 filters, 3x3
+7. Max pooling, 2x2
+8. KAGN Convolution, 256 filters, 3x3
+9. KAGN Convolution, 256 filters, 3x3
+10 Max pooling, 2x2
+11. KAGN Convolution, 256 filters, 3x3
+12. KAGN Convolution, 256 filters, 3x3
+13. Max pooling, 2x2
+14. KAGN Convolution, 512 filters, 3x3
+15. KAGN Convolution, 512 filters, 3x3
+16. Global Average pooling
+17. Output layer, 1000 nodes.
+
+![model image](https://github.com/IvanDrokin/torch-conv-kan/blob/main/assets/vgg_kagn_11_v4.png?raw=true)
+
+
+## Intended uses & limitations
+
+You can use the raw model for image classification or use it as pretrained model for further finetuning.
+
+### How to use
+
+First, clone the repository:
+
+```
+git clone https://github.com/IvanDrokin/torch-conv-kan.git
+cd torch-conv-kan
+pip install -r requirements.txt
+```
+Then you can initialize the model and load weights.
+
+```python
+import torch
+from models import vggkagn
+model = vggkagn(3,
+                1000,
+                groups=1,
+                degree=5,
+                dropout=0.15,
+                l1_decay=0,
+                dropout_linear=0.25,
+                width_scale=2,
+                vgg_type='VGG11v4',
+                expected_feature_shape=(1, 1),
+                affine=True
+                )
+model.from_pretrained('brivangl/vgg_kagn11_v4')
+```
+
+Transforms, used for validation on Imagenet1k:
+
+```python
+from torchvision.transforms import v2
+transforms_val = v2.Compose([
+        v2.ToImage(),
+        v2.Resize(256, antialias=True),
+        v2.CenterCrop(224),
+        v2.ToDtype(torch.float32, scale=True),
+        v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+```
+
+
+
+## Training data
+This model trained on Imagenet1k dataset (1281167 images in train set)
+
+## Training procedure
+
+Model was trained during 200 full epochs with AdamW optimizer, with following parameters:
+```python
+{'learning_rate': 0.0009, 'adam_beta1': 0.9, 'adam_beta2': 0.999, 'adam_weight_decay': 5e-06,
+'adam_epsilon': 1e-08, 'lr_warmup_steps': 7500, 'lr_power': 0.3, 'lr_end': 1e-07, 'set_grads_to_none': False}
+```
+And this augmnetations:
+```python
+transforms_train = v2.Compose([
+    v2.ToImage(),
+    v2.RandomHorizontalFlip(p=0.5),
+    v2.RandomResizedCrop(224, antialias=True),
+    v2.RandomChoice([v2.AutoAugment(AutoAugmentPolicy.CIFAR10),
+                     v2.AutoAugment(AutoAugmentPolicy.IMAGENET)
+                     ]),
+    v2.ToDtype(torch.float32, scale=True),
+    v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+```
+
+## Evaluation results
+
+On Imagenet1k Validation:
+
+| Accuracy, top1 | Accuracy, top5 | AUC (ovo) | AUC (ovr) |
+|:--------------:|:--------------:|:---------:|:---------:|
+|      61.17     |      83.26     |   99.42   |   99.43   |
+
+On Imagenet1k Test:
+Coming soon
+
+### BibTeX entry and citation info
+
+If you use this project in your research or wish to refer to the baseline results, please use the following BibTeX entry.
+
+```bibtex
+@misc{torch-conv-kan,
+  author = {Ivan Drokin},
+  title = {Torch Conv KAN},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/IvanDrokin/torch-conv-kan}}
+}
+```
+
+## References
+
+- [1] Ziming Liu et al., "KAN: Kolmogorov-Arnold Networks", 2024, arXiv. https://arxiv.org/abs/2404.19756
+- [2] https://github.com/KindXiaoming/pykan
+- [3] https://github.com/Khochawongwat/GRAMKAN