README.md

---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
language:
- en
library_name: transformers
pipeline_tag: image-to-text
tags:
- blip
- image-captioning
- vision-language
- flickr8k
- coco
license: bsd-3-clause
datasets:
- ariG23498/flickr8k
- yerevann/coco-karpathy
base_model: Salesforce/blip-image-captioning-base
---

# Model Card for Image-Captioning-BLIP (Fine‑Tuned BLIP for Image Captioning)

<!-- Provide a quick summary of what the model is/does. -->

This repository provides a lightweight, pragmatic **fine‑tuning and evaluation pipeline around Salesforce BLIP** for image captioning, with sane defaults and a tiny, production‑friendly inference helper. Use it to fine‑tune `Salesforce/blip-image-captioning-base` on **Flickr8k** or **COCO‑Karpathy** and export artifacts you can push to the Hugging Face Hub.

> **TL;DR**: End‑to‑end train → evaluate → export → caption images with a few commands. Defaults: BLIP‑base (ViT‑B/16), Flickr8k, BLEU during training, COCO‑style metrics (CIDEr/METEOR/SPICE) after training.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This project fine‑tunes **BLIP (Bootstrapping Language‑Image Pre-training)** for the **image‑to‑text** task. BLIP couples a ViT visual encoder with a text decoder for conditional generation and uses a bootstrapped captioning strategy during pretraining in the original work. Here, we re‑use the open **`BlipForConditionalGeneration`** weights and processor and adapt them to caption everyday photographs from Flickr8k or the COCO Karpathy split.

- **Developed by:** Amirhossein Yousefi  
- **Shared by :** Amirhossein Yousefi  
- **Model type:** Vision–language encoder–decoder (BLIP base; ViT‑B/16 vision encoder + text decoder)  
- **Language(s) (NLP):** English  
- **License:** BSD‑3‑Clause (inherits from the base model’s license; ensure your own dataset/weight licensing is compatible)  
- **Finetuned from model :** `Salesforce/blip-image-captioning-base`

### Model Sources 

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/amirhossein-yousefi/Image-Captioning-BLIP  
- **Paper :** BLIP — Bootstrapping Language‑Image Pre‑training (arXiv:2201.12086) https://arxiv.org/abs/2201.12086  
- **Demo :** See usage examples in the base model card on the Hub (PyTorch snippets)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

- Generate concise alt‑text‑style captions for photos.  
- Zero‑shot captioning with the base checkpoint, or improved fidelity after fine‑tuning on your target dataset.  
- Batch/offline captioning for indexing, search, and accessibility workflows.

### Downstream Use 

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

- Warm‑start other captioners or retrieval models by using generated captions as weak labels.  
- Build dataset bootstrapping pipelines (e.g., pseudo‑labels for new domains).  
- Use as a component in multi‑modal applications (e.g., visual content tagging, basic scene summaries).

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

- High‑stakes or safety‑critical settings (medical, legal, surveillance).  
- Factual description of specialized imagery (e.g., diagrams, medical scans) without domain‑specific fine‑tuning.  
- Content moderation, protected‑attribute inference, or demographic classification.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

- **Data bias:** Flickr8k/COCO contain Western‑centric scenes and captions; captions may reflect annotator bias or stereotypes.  
- **Language coverage:** Training here targets English only; captions for non‑English content or localized entities may be poor.  
- **Hallucination:** Like most captioners, BLIP can produce plausible but incorrect or over‑confident statements.  
- **Privacy:** Avoid using on sensitive images or personally identifiable content without consent.  
- **IP & license:** Ensure you have rights to your training/evaluation images and that your dataset use complies with its license.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

- Evaluate on a **domain‑specific validation set** before deployment.  
- Use a **safety filter**/keyword blacklist or human review if captions are user‑facing.  
- For specialized domains, **continue fine‑tuning** with in‑domain images and style prompts.  
- When summarizing scenes, prefer **beam search** with moderate length penalties and enforce max lengths to curb rambling.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

# Replace with your fine-tuned repo once pushed, e.g. "amirhossein-yousefi/blip-captioning-flickr8k"
MODEL_ID = "Salesforce/blip-image-captioning-base"

processor = BlipProcessor.from_pretrained(MODEL_ID)
model = BlipForConditionalGeneration.from_pretrained(MODEL_ID)

image = Image.open("example.jpg").convert("RGB")
inputs = processor(image, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=30, num_beams=5, length_penalty=1.0, early_stopping=True)
print(processor.decode(out[0], skip_special_tokens=True))
```
## Training Details

### Training Data

Two common options are wired in:

- **Flickr8k** (`ariG23498/flickr8k`) — 8k images with 5 captions each. Default split in this repo: **90% train / 5% val / 5% test** (deterministic by seed).
- **COCO‑Karpathy** (`yerevann/coco-karpathy`) — community‑prepared Karpathy splits for COCO captions.

> ⚠️ Always verify dataset licenses and usage terms before training or publishing models derived from them.

### Training Procedure

This project uses the Hugging Face **Trainer** with a custom collator; `BlipProcessor` handles both image and text preprocessing, and labels are padded to `-100` for loss masking.

#### Preprocessing

- Images and text are preprocessed by `BlipProcessor` consistent with BLIP defaults (resize/normalize/tokenize).
- Optional **vision encoder freezing** is supported for parameter‑efficient fine‑tuning.

#### Training Hyperparameters (defaults)

- **Epochs:** `4`
- **Learning rate:** `5e-5`
- **Per‑device batch size:** `8` (train & eval)
- **Gradient accumulation:** `2`
- **Gradient checkpointing:** `True`
- **Freeze vision encoder:** `False` (set `True` for low‑VRAM setups)
- **Logging:** every `50` steps; keep `2` checkpoints
- **Model selection:** best `sacrebleu`

#### Generation (eval/inference defaults)

- `max_txt_len = 40`, `gen_max_new_tokens = 30`, `num_beams = 5`, `length_penalty = 1.0`, `early_stopping = True`

#### Speeds, Sizes, Times

- **Single 16 GB GPU** is typically sufficient for BLIP‑base with the defaults (gradient checkpointing enabled).
- If VRAM is tight: freeze the vision encoder, lower the batch size, and/or increase gradient accumulation.

## Evaluation

### Testing Data, Factors & Metrics

- **Data:** Validation split of the chosen dataset (Flickr8k or COCO‑Karpathy).
- **Metrics:** BLEU‑4 (during training), and post‑training **COCO‑style metrics**: **CIDEr**, **METEOR**, **SPICE**.
- **Notes:** SPICE requires Java and can be slow; you can disable or subsample via config.

### Results

After training, a compact JSON with COCO metrics is written to:

```
blip-open-out/coco_metrics.json
```
## 🏆 Results (Test Split)

<p align="center">
  <img alt="BLEU4" src="https://img.shields.io/badge/BLEU4-0.9708-2f81f7?style=for-the-badge">
  <img alt="METEOR" src="https://img.shields.io/badge/METEOR-0.7888-8a2be2?style=for-the-badge">
  <img alt="CIDEr" src="https://img.shields.io/badge/CIDEr-9.333-0f766e?style=for-the-badge">
  <img alt="SPICE" src="https://img.shields.io/badge/SPICE-n%2Fa-lightgray?style=for-the-badge">
</p>

| Metric    | Score |
|-----------|------:|
| BLEU‑4    | **0.9708** |
| METEOR    | **0.7888** |
| CIDEr     | **9.3330** |
| SPICE     | — |

<details>
<summary>Raw JSON</summary>

```json
{
  "Bleu_4": 0.9707865195383757,
  "METEOR": 0.7887653835397767,
  "CIDEr": 9.332990983959254,
  "SPICE": null
}
```
</details>
---


#### Summary

- Expect strongest results when fine‑tuning on in‑domain imagery and using beam search at inference time.

## Model Examination

- Inspect failure cases: cluttered scenes, occlusions, specialized objects, or images with embedded text.
- Run **qualitative sweeps** by toggling beam size and length penalties to see style/verbosity changes.

## Environmental Impact

Estimate using the [ML CO2 Impact calculator](https://mlco2.github.io/impact#compute). Fill the values you observe for your runs:

- **Hardware Type:** (e.g., 1× NVIDIA T4 / A10 / A100)
- **Hours used:** (e.g., 3.2 h for 4 epochs on Flickr8k)
- **Cloud Provider:** (e.g., AWS on SageMaker, optional)
- **Compute Region:** (e.g., us‑west‑2)
- **Carbon Emitted:** (estimated grams of CO₂eq)

## Technical Specifications

### Model Architecture and Objective

- **Architecture:** BLIP encoder–decoder; **ViT‑B/16** vision backbone with a text decoder for conditional caption generation.
- **Objective:** Cross‑entropy on tokenized captions with masked padding (`-100`), using the BLIP processor’s packing.

### Compute Infrastructure

#### Hardware

- Trains comfortably on **one 16 GB GPU** (defaults).

#### Software

- **Python 3.9+**, **PyTorch**, **Transformers**, **Datasets**, **evaluate**, **sacrebleu**, optional **pycocotools/pycocoevalcap** (for CIDEr/METEOR/SPICE).
- Optional **AWS SageMaker** entry points are included for managed training and inference.