--- language: - en tags: - image-to-text license: mit datasets: - coco2017 --- # Vit2-DistilGPT2 This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in [this kaggle kernel](https://www.kaggle.com/sachin/visionencoderdecoder-model-training) ## Usage ```python import Image from transformers import AutoModel, GPT2Tokenizer, ViTFeatureExtractor model = AutoModel.from_pretrained("sachin/vit2distilgpt2") vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") # make sure GPT2 appends EOS in begin and end def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] return outputs GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens gpt2_tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2") # set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token image = (Image.open(image_path).convert("RGB"), return_tensors="pt").pixel_values encoder_outputs = model.generate(image.unsqueeze(0)) generated_sentences = gpt2_tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True) ``` Note that the output sentence may be repeated, hence a post processing step may be required. ## Bias Warning This model may be biased due to dataset, lack of long training and the model itself. The following gender bias is an example. ![](https://i.imgur.com/9zVN022.png) ## Results