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--- |
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tags: |
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- image-to-text |
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- image-captioning |
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license: apache-2.0 |
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widget: |
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- src: https://pixabay.com/get/ga187b8f146a9fa30b1f553d63fa94271e023868cd247fbad7ce02b6ffb5718a52fc04809be440f997f57dad90614dde2e9821edf8e628925f0042c6584fc04ec809421a040e3bc9561324249ab6e09c4_1280.jpg |
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example_title: Horse Riding |
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- src: https://static1.bigstockphoto.com/6/8/2/large1500/286059499.jpg |
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example_title: Bicycle |
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--- |
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This is an image captioning model training by Zayn |
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```python |
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from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer |
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model = VisionEncoderDecoderModel.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically") |
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feature_extractor = ViTFeatureExtractor.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically") |
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tokenizer = AutoTokenizer.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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max_length = 20 |
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num_beams = 8 |
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gen_kwargs = {"max_length": max_length, "num_beams": num_beams} |
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def predict_step(image_paths): |
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images = [] |
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for image_path in image_paths: |
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i_image = Image.open(image_path) |
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if i_image.mode != "RGB": |
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i_image = i_image.convert(mode="RGB") |
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images.append(i_image) |
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pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values |
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pixel_values = pixel_values.to(device) |
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output_ids = model.generate(pixel_values, **gen_kwargs) |
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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preds = [pred.strip() for pred in preds] |
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return preds |
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predict_step(['Image URL.jpg']) |
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