Update README.md

README.md

@@ -7,4 +7,92 @@ tags:
 - clip
 - genshin-impact
 - game
----
+---
+
+# GenshinCLIP
+A simple and small-size open-sourced CLIP model fine-tuned on Genshin Impact's image-text pairs.
+
+Visit the [github](https://github.com/mrzjy/GenshinCLIP) for case study and data pair examples.
+
+The model is far from being perfect, but could still offer some better text-image matching performance in some Genshin Impact scenarios.
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like zero-shot image classification and image-text retrieval.
+
+### How to use (With OpenCLIP)
+
+Here is how to use this model to perform zero-shot image classification:
+
+```python
+import torch
+import torch.nn.functional as F
+from PIL import Image
+import requests
+from open_clip import create_model_from_pretrained, get_tokenizer
+
+def preprocess_text(string):
+    return "Genshin Impact\n" + string
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# load checkpoint from local path
+# model_path = "path/to/open_clip_pytorch_model.bin"
+# model_name = "ViT-SO400M-14-SigLIP-384"
+# model, preprocess = create_model_from_pretrained(model_name=model_name, pretrained=model_path, device=device)
+# tokenizer = get_tokenizer(model_name)
+
+# or load from hub
+model, preprocess = create_model_from_pretrained('hf-hub:mrzjy/GenshinImpact-CLIP-ViT-B-16-laion2B-s34B-b88K')
+tokenizer = get_tokenizer('hf-hub:mrzjy/GenshinImpact-CLIP-ViT-B-16-laion2B-s34B-b88K')
+
+# image
+image_url = "https://static.wikia.nocookie.net/gensin-impact/images/3/33/Qingce_Village.png"
+image = Image.open(requests.get(image_url, stream=True).raw)
+image = preprocess(image).unsqueeze(0).to(device)
+
+# text choices
+labels = [
+    "This is an area of Liyue",
+    "This is an area of Mondstadt",
+    "This is an area of Sumeru",
+    "This is Qingce Village"
+]
+labels = [preprocess_text(l) for l in labels]
+text = tokenizer(labels, context_length=model.context_length).to(device)
+with torch.autocast(device_type=device.type):
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+        text_features = model.encode_text(text)
+        image_features = F.normalize(image_features, dim=-1)
+        image_features = F.normalize(image_features, dim=-1)
+        text_features = F.normalize(text_features, dim=-1)
+        text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
+        scores = [f"{s:.3f}" for i, s in enumerate(text_probs.tolist()[0])]
+        print(scores)  # [0.016, 0.000, 0.001, 0.233]
+```
+
+## Model Card
+### CLIP for GenshinImpact
+
+[CLIP model](https://huggingface.co/laion/CLIP-ViT-B-16-laion2B-s34B-b88K) further fine-tuned on 17k Genshin Impact English text-image pairs at resolution 384x384.
+
+### Training data description
+
+There're currently 17,428 (train) and 918 (validation) text-image pairs used for model training.
+
+All the images and texts are crawled from [Genshin Fandom Wiki](https://genshin-impact.fandom.com/wiki) and are manually parsed to form text-image pairs.
+
+**Image Processing:**
+- Size: Resize all images to 384x384 pixels to match the original model training settings.
+- Format: Accept images in PNG or GIF format. For GIFs, extract a random frame to create a static image for text-image pairs.
+
+**Text Processing:**
+- Source: Text can be from the simple caption attribute of an HTML `<img>` tag or specified web content.
+- Format: Prepend all texts with "Genshin Impact" along with some simple template to form natural language sentences.
+
+**Data Distribution:**
+
+![data_distribution.png](img%2Fdata_distribution.png)
+
+**Validation Loss Curve**