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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: open_clip |
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pipeline_tag: zero-shot-image-classification |
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tags: |
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- clip |
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- genshin-impact |
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- game |
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- siglip |
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--- |
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# GenshinCLIP |
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A simple open-sourced SigLIP model fine-tuned on Genshin Impact's image-text pairs. |
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Visit the [github](https://github.com/mrzjy/GenshinCLIP) for case study and data pair examples. |
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The model is far from being perfect, but could still offer some better text-image matching performance in some Genshin Impact scenarios. |
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| Model | Checkpoint Size | Val Loss | |
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|:-------------------------------------------------------------------------------------------:|:-----------------:|:----------:| |
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| [GenshinImpact-CLIP-ViT-B-16-laion2B-s34B-b88K](https://huggingface.co/mrzjy/GenshinImpact-CLIP-ViT-B-16-laion2B-s34B-b88K) | 0.59 GB | 1.152 | |
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| [GenshinImpact-ViT-SO400M-14-SigLIP-384](https://huggingface.co/mrzjy/GenshinImpact-ViT-SO400M-14-SigLIP-384) | 3.51 GB | 0.362 | |
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## Intended uses & limitations |
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You can use the raw model for tasks like zero-shot image classification and image-text retrieval. |
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### How to use (With OpenCLIP) |
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Here is how to use this model to perform zero-shot image classification: |
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```python |
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import torch |
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import torch.nn.functional as F |
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from PIL import Image |
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import requests |
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from open_clip import create_model_from_pretrained, get_tokenizer |
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def preprocess_text(string): |
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return "Genshin Impact\n" + string |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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# load checkpoint from local path |
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# model_path = "path/to/open_clip_pytorch_model.bin" |
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# model_name = "ViT-SO400M-14-SigLIP-384" |
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# model, preprocess = create_model_from_pretrained(model_name=model_name, pretrained=model_path, device=device) |
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# tokenizer = get_tokenizer(model_name) |
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# or load from hub |
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model, preprocess = create_model_from_pretrained('hf-hub:mrzjy/GenshinImpact-ViT-SO400M-14-SigLIP-384') |
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tokenizer = get_tokenizer('hf-hub:mrzjy/GenshinImpact-ViT-SO400M-14-SigLIP-384') |
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# image |
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image_url = "https://static.wikia.nocookie.net/gensin-impact/images/3/33/Qingce_Village.png" |
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image = Image.open(requests.get(image_url, stream=True).raw) |
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image = preprocess(image).unsqueeze(0).to(device) |
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# text choices |
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labels = [ |
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"This is an area of Liyue", |
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"This is an area of Mondstadt", |
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"This is an area of Sumeru", |
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"This is Qingce Village" |
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] |
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labels = [preprocess_text(l) for l in labels] |
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text = tokenizer(labels, context_length=model.context_length).to(device) |
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with torch.autocast(device_type=device.type): |
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with torch.no_grad(): |
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image_features = model.encode_image(image) |
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text_features = model.encode_text(text) |
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image_features = F.normalize(image_features, dim=-1) |
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image_features = F.normalize(image_features, dim=-1) |
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text_features = F.normalize(text_features, dim=-1) |
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text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) |
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scores = [f"{s:.3f}" for i, s in enumerate(text_probs.tolist()[0])] |
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print(scores) # [0.016, 0.000, 0.001, 0.233] |
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``` |
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## Model Card |
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### SigLIP for GenshinImpact |
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[SigLIP model](https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384) further fine-tuned on 17k Genshin Impact English text-image pairs at resolution 384x384. |
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### Training data description |
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There're currently 17,428 (train) and 918 (validation) text-image pairs used for model training. |
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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. |
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**Image Processing:** |
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- Size: Resize all images to 384x384 pixels to match the original model training settings. |
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- Format: Accept images in PNG or GIF format. For GIFs, extract a random frame to create a static image for text-image pairs. |
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**Text Processing:** |
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- Source: Text can be from the simple caption attribute of an HTML `<img>` tag or specified web content. |
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- Format: Prepend all texts with "Genshin Impact" along with some simple template to form natural language sentences. |
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**Data Distribution:** |
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**Validation Loss Curve** |
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