Updated README.md

#2
by hvt4 - opened
Files changed (1) hide show
  1. README.md +23 -6
README.md CHANGED
@@ -31,10 +31,6 @@ model-index:
31
 
32
  VGG11 model pre-trained on ImageNet-1k. Originally introduced by Karen Simonyan and Andrew Zisserman in the influential paper, [**Very Deep Convolutional Networks for Large-Scale Image Recognition**](https://arxiv.org/abs/1409.1556) this configuration serves as the base 8-layer convolutional architecture (plus 3 fully connected layers) that proved the effectiveness of using small $3 \times 3$ filters to build deep networks while maintaining a manageable number of parameters.
33
 
34
- ## Intended uses & limitations
35
-
36
- The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
37
-
38
 
39
  ## Model description
40
 
@@ -45,9 +41,24 @@ acc@1 (on ImageNet-1K): 69.02%
45
  acc@5 (on ImageNet-1K): 88.628%
46
  num_params: 132863336
47
 
48
- The license information of the original model was missing.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
49
 
50
- ## Use
51
  ```python
52
  #!/usr/bin/env python3
53
  import argparse, json
@@ -107,6 +118,12 @@ def main():
107
  if __name__ == "__main__":
108
  main()
109
  ```
 
 
 
 
 
 
110
  ### BibTeX entry and citation info
111
 
112
  ```bibtex
 
31
 
32
  VGG11 model pre-trained on ImageNet-1k. Originally introduced by Karen Simonyan and Andrew Zisserman in the influential paper, [**Very Deep Convolutional Networks for Large-Scale Image Recognition**](https://arxiv.org/abs/1409.1556) this configuration serves as the base 8-layer convolutional architecture (plus 3 fully connected layers) that proved the effectiveness of using small $3 \times 3$ filters to build deep networks while maintaining a manageable number of parameters.
33
 
 
 
 
 
34
 
35
  ## Model description
36
 
 
41
  acc@5 (on ImageNet-1K): 88.628%
42
  num_params: 132863336
43
 
44
+ ## Intended uses & limitations
45
+
46
+ The model files were converted from pretrained weights from PyTorch Vision. The models may have their own licenses or terms and conditions derived from PyTorch Vision and the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
47
+
48
+
49
+ ## How to Use
50
+
51
+ **1. Install Dependencies** Ensure your Python environment is set up with the required libraries. Run the following command in your terminal:
52
+
53
+ ```bash
54
+ pip install numpy Pillow huggingface_hub ai-edge-litert
55
+ ```
56
+
57
+ **2. Prepare Your Image** The script expects an image file to analyze. Make sure you have an image (e.g., cat.jpg or car.png) saved in the same working directory as your script.
58
+
59
+ **3. Save the Script** Create a new file named `classify.py`, paste the script below into it, and save the file:
60
+
61
 
 
62
  ```python
63
  #!/usr/bin/env python3
64
  import argparse, json
 
118
  if __name__ == "__main__":
119
  main()
120
  ```
121
+
122
+ **4. Execute the Python Script** Run the below command:
123
+
124
+ ```bash
125
+ python classify.py --image cat.jpg
126
+ ```
127
  ### BibTeX entry and citation info
128
 
129
  ```bibtex