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---
license: apache-2.0
---
CLIP model post-trained on 80M human face images.
Trained with [TencentPretrain](https://github.com/Tencent/TencentPretrain) framework on 8 * A100 GPUs:
```
python3 pretrain.py --dataset_path faceclip.pt \
--pretrained_model_path models/clip-b32.bin \
--output_model_path models/faceclip-b32.bin \
--config_path models/clip/base-32_config.json \
--vocab_path vocab.json --merges_path merges.txt --tokenizer clip \
--world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 --data_processor clip --accumulation_steps 8 --learning_rate 2e-5 \
--total_steps 200000 --save_checkpoint_steps 20000 --batch_size 160 --report_steps 500
```
How to use:
```
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("P01son/FaceCLIP-base-32")
processor = CLIPProcessor.from_pretrained("P01son/FaceCLIP-base-32")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
``` |