
## Overview

This model is based on [CLIP](https://openai.com/blog/clip) model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can be dataset labels or other description.

## Text-image matching 

### Model Input

```python
Class = ["dog", "cat", "rabbit","squirrel"]
image = preprocess(Image.open("/content/drive/MyDrive/Transformer_CLIP/Golden_Retriever.jpeg")).unsqueeze(0).to(device)
text = clip.tokenize(Class).to(device)
```

### Model Process

```python
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    
    logits_per_image, logits_per_text = model(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

res = 0
pos = -1
for j in range(len(probs[0])):
  if probs[0][j] > res:
    res = probs[0][j]
    pos = j


print("The options available are: " + str(Class))
for i in range(len(probs[0])):
  print("The probability of " + str(Class[i]) + " is " + str(probs[0][i]))

print("Model thinks this photo is most likely a " + Class[pos])
```

## Animal Classification

### Demo code (Python Notebook)
https://huggingface.co/Haofeng/CLIP_animal_classification/blob/main/CLIP_classfication.ipynb
https://huggingface.co/Haofeng/CLIP_animal_classification/blob/main/CLIP_classfication10.ipynb

### Dataset reference
https://www.kaggle.com/datasets/alessiocorrado99/animals10
https://www.kaggle.com/datasets/ayushv322/animal-classification
{"license": "mit"}
license:mit has_space region:us
TAGS
#license-mit #has_space #region-us 


## Overview

This model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can be dataset labels or other description.

## Text-image matching 

### Model Input



### Model Process



## Animal Classification

### Demo code (Python Notebook)
URL
URL

### Dataset reference
URL
URL
## Overview

This model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can be dataset labels or other description. ## Text-image matching ### Model Input ### Model Process ## Animal Classification ### Demo code (Python Notebook)
URL
URL ### Dataset reference
URL
URL
TAGS
#license-mit #has_space #region-us 
 ## Overview

This model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can be dataset labels or other description. ## Text-image matching ### Model Input ### Model Process ## Animal Classification ### Demo code (Python Notebook)
URL
URL ### Dataset reference
URL
URL
passage: TAGS
#license-mit #has_space #region-us 
## Overview

This model is based on CLIP model and test on four kinds of animal datasets and ten kinds of animal datasets. CLIP model is a zero-shot pre-trained model so we don't need train model. We just input possible classes and image dataset to use model. Possible classes can be defined by yourself, it can be dataset labels or other description.## Text-image matching### Model Input### Model Process## Animal Classification### Demo code (Python Notebook)
URL
URL### Dataset reference
URL
URL