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README.md

@@ -1,138 +1,9 @@
 ---
-license: mit
-datasets:
-- imagenet1k
-metrics:
-- accuracy
+tags:
+- pytorch_model_hub_mixin
+- model_hub_mixin
 ---
-# VGG-like Kolmogorov-Arnold Convolutional network with Gram polynomials
 
-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 Bottleneck Gram ConvKAN Layers with BatchNorm, 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_bn
-model = vggkagn_bn(3,
-                   1000,
-                   groups=1,
-                   degree=5,
-                   dropout=0.05,
-                   l1_decay=0,
-                   width_scale=2,
-                   affine=True,
-                   norm_layer=nn.BatchNorm2d,
-                   expected_feature_shape=(1, 1),
-                   vgg_type='VGG11v4')
-model.from_pretrained('brivangl/vgg_kagn_bn11_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': 0.0005, '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) |
-|:--------------:|:--------------:|:---------:|:---------:|
-|      68.50     |      88.46     |   99.61   |   99.61   |
-
-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
+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]