Image-to-Text
Transformers
ONNX
Safetensors
vision-encoder-decoder
image-text-to-text
image-captioning
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("tarekziade/deit-tiny-distilgpt2")
model = AutoModelForMultimodalLM.from_pretrained("tarekziade/deit-tiny-distilgpt2", device_map="auto")Quick Links
Variation of https://huggingface.co/tarekziade/distilvit
Trained on 270k images from Flickr10k and COCO. Training source code: https://github.com/tarekziade/distilvit
Results:
- eval_loss: 0.2305169701576233
- eval_rouge1: 39.511
- eval_rouge2: 14.7798
- eval_rougeL: 35.9476
- eval_rougeLsum: 35.9497
- eval_gen_len: 11.695219762592236
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Model tree for tarekziade/deit-tiny-distilgpt2
Base model
distilbert/distilgpt2
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="tarekziade/deit-tiny-distilgpt2")