Aman Sharma
commited on
Commit
Β·
105870d
1
Parent(s):
8462fc9
gradio demo added
Browse files
README.md
CHANGED
@@ -11,3 +11,56 @@ license: mit
|
|
11 |
---
|
12 |
|
13 |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
11 |
---
|
12 |
|
13 |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
14 |
+
|
15 |
+
# Sketch NN: Neural Network Designer
|
16 |
+
|
17 |
+
<div align="center">
|
18 |
+
<img src="path_to_your_logo.png" alt="Sketch NN Logo" width="200"/>
|
19 |
+
<h3>Design Neural Networks with a Simple Sketch</h3>
|
20 |
+
</div>
|
21 |
+
|
22 |
+
Sketch NN is a powerful tool that allows you to design and generate PyTorch neural network code from simple flowchart sketches. With Sketch NN, you can quickly prototype complex neural architectures without writing a single line of code!
|
23 |
+
|
24 |
+
## π Features
|
25 |
+
|
26 |
+
- πΈ Upload or capture flowchart images
|
27 |
+
- π§ Supports a wide range of neural network layers
|
28 |
+
- π§ Generates ready-to-use PyTorch code
|
29 |
+
- π₯οΈ User-friendly Gradio web interface
|
30 |
+
- π FastAPI backend for scalable deployment
|
31 |
+
|
32 |
+
## π οΈ Supported Layers
|
33 |
+
|
34 |
+
- Convolutional (Conv2D)
|
35 |
+
- Pooling (MaxPool2D, AvgPool2D)
|
36 |
+
- Fully Connected (Linear)
|
37 |
+
- Batch Normalization
|
38 |
+
- Dropout
|
39 |
+
- Activation Functions (ReLU, LeakyReLU, Sigmoid, Tanh)
|
40 |
+
- Recurrent (LSTM, GRU)
|
41 |
+
- Transformer
|
42 |
+
- Multi-head Attention
|
43 |
+
|
44 |
+
## πΌοΈ How It Works
|
45 |
+
|
46 |
+
[Insert a diagram or flowchart here showing the process from sketch to code]
|
47 |
+
|
48 |
+
1. Sketch your neural network architecture
|
49 |
+
2. Upload or capture the image
|
50 |
+
3. Sketch NN processes the image and extracts layer information
|
51 |
+
4. PyTorch code is generated based on the extracted information
|
52 |
+
5. Download and use the generated code in your project
|
53 |
+
|
54 |
+
## π Getting Started
|
55 |
+
|
56 |
+
### Prerequisites
|
57 |
+
|
58 |
+
- Python 3.7+
|
59 |
+
- PyTorch
|
60 |
+
- OpenCV
|
61 |
+
- Tesseract OCR
|
62 |
+
|
63 |
+
### Installation
|
64 |
+
|
65 |
+
```bash
|
66 |
+
pip install sketch-nn
|