Update README.md

README.md

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 # VGG-like Kolmogorov-Arnold Convolutional network with Gram polynomials
 
-This model is a convolutional version of Kolmogorov-Arnold Network, originally presented in [1, 2], with architecture inspired by VGG11 model. Gram version of KAN originally presented in [3].
+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, 256 filters, 3x3
+15. KAGN Convolution, 256 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_v2.png)
+
+
 ## 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='VGG11v2',
+                expected_feature_shape=(1, 1),
+                affine=True
+                )
 
+model.from_pretrained('Brivangl/vgg_kagn11_v2')
 ```
 
+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) |
+|:--------------:|:--------------:|:---------:|:---------:|
+|      59.1      |      82.29     |   99.43   |   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.