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README.md

@@ -1,3 +1,158 @@
 ---
-license: afl-3.0
+pipeline_tag: other
+tags:
+- image-captioning
+inference: false
+languages:
+- en
+license: bsd-3-clause
+datasets:
+- ybelkada/football-dataset
 ---
+
+# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
+
+Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone) - and fine-tuned on
+[football dataset](https://huggingface.co/datasets/ybelkada/football-dataset).
+
+Google Colab notebook for fine-tuning: https://colab.research.google.com/drive/1lbqiSiA0sDF7JDWPeS0tccrM85LloVha?usp=sharing 
+
+| ![BLIP.gif](https://s3.amazonaws.com/moonup/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
+|:--:|
+| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
+
+## TL;DR
+
+Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
+
+*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
+
+## Usage
+
+You can use this model for conditional and un-conditional image captioning
+
+### Using the Pytorch model
+
+#### Running the model on CPU
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("ybelkada/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("ybelkada/blip-image-captioning-base")
+
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+# >>> a photography of a woman and her dog
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach with her dog
+```
+</details>
+
+#### Running the model on GPU
+
+##### In full precision 
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesfoce/blip-image-captioning-base").to("cuda")
+
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt").to("cuda")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+# >>> a photography of a woman and her dog
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt").to("cuda")
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach with her dog
+```
+</details>
+
+##### In half precision (`float16`)
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
+
+img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+
+# conditional image captioning
+text = "a photography of"
+inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+# >>> a photography of a woman and her dog
+
+# unconditional image captioning
+inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+
+out = model.generate(**inputs)
+print(processor.decode(out[0], skip_special_tokens=True))
+>>> a woman sitting on the beach with her dog
+```
+</details>
+
+## BibTex and citation info
+
+```
+@misc{https://doi.org/10.48550/arxiv.2201.12086,
+  doi = {10.48550/ARXIV.2201.12086},
+  
+  url = {https://arxiv.org/abs/2201.12086},
+  
+  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
+  
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

config.json

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+{
+  "_commit_hash": "3007fbe8c3bbd0b8121111dee8e8d06c0f2783c9",
+  "_name_or_path": "Salesforce/blip-image-captioning-base",
+  "architectures": [
+    "BlipForConditionalGeneration"
+  ],
+  "image_text_hidden_size": 256,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "blip",
+  "projection_dim": 512,
+  "text_config": {
+    "_name_or_path": "",
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+    "finetuning_task": null,
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+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.0,
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": true,
+    "is_encoder_decoder": false,
+    "label2id": {
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+    "length_penalty": 1.0,
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+    "max_position_embeddings": 512,
+    "min_length": 0,
+    "model_type": "blip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 8,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 12,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
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+    "pruned_heads": {},
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+    "temperature": 1.0,
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+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "use_cache": true,
+    "vocab_size": 30524
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
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+    "hidden_act": "gelu",
+    "hidden_size": 768,
+    "id2label": {
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+      "1": "LABEL_1"
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+    "initializer_factor": 1.0,
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+    "use_bfloat16": false
+  }
+}

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