This model generates a natural-language description of an image. It uses a ResNet50 spatial-feature encoder, a Bahdanau (additive) attention module, and a GRU decoder initialized with GloVe 6B 300d embeddings, trained on the merged Flickr8k + Flickr30k dataset (39,874 images × 5 captions). It follows the architecture from Show, Attend and Tell (Xu et al., 2015) with label smoothing, scheduled sampling, and two-phase CNN fine-tuning.

Test-set performance (beam search, k = 5)

Evaluated on the held-out 1,873-image test split (image-level split — no captions cross train/val/test).

Metric	Value
BLEU-1	0.6859
BLEU-2	0.5289
BLEU-3	0.4041
BLEU-4	0.3093
METEOR	0.4709
CIDEr	0.7961
ROUGE-L	0.5257

Beam search uses length-normalized log-probs (alpha = 0.7) and a repetition penalty of 1.2.

Architecture

Image (3, 224, 224)
  └─ ResNet50 (pretrained, frozen first 10 epochs, last 2 blocks fine-tuned)
       output: (B, 2048, 7, 7)  → reshape to (B, 49, 2048)
  └─ Bahdanau attention  V·tanh(W_enc(features) + W_dec(h_prev))
       output: context vector (B, 2048), attention weights (B, 49)
  └─ GRUCell  (per timestep — re-queries attention each step)
       hidden state size: 1024, embedding size: 300 (GloVe 6B 300d)
  └─ Linear → vocab logits (V = 10,111)

Total parameters: ~37 M (25 M frozen ResNet, 12 M trainable decoder/projection).

Training details

Dataset — Flickr8k + Flickr30k merged (37,000 train / 1,000 val / 1,873 test)
Vocabulary — 10,111 tokens (frequency threshold 3), built from train captions only. Special tokens: <pad>=0, <start>=1, <end>=2, =3`.
Loss — CrossEntropyLoss(ignore_index=0, label_smoothing=0.1) plus doubly-stochastic regularization α_c · ((1 − Σ_t α_t)²).mean() with α_c = 1.0
Optimizer — Adam, decoder LR 3.2e-3, encoder LR 8e-5 (Phase B)
Schedule — ReduceLROnPlateau on val BLEU-4, factor=0.5, patience=3
Two-phase training — Phase A (epochs 1–10): freeze CNN. Phase B (epochs 11–35): unfreeze last 2 ResNet blocks.
Scheduled sampling — linear ramp from 0 to max 0.25 over training epochs
Batch size — 256, gradient clip 5.0, seed 42

Files in this repo

attention_gru_glove.pth — PyTorch checkpoint (encoder + decoder state dicts, config)
vocab.pkl — pickled Vocabulary object built from the train split
config.json — JSON copy of the training hyperparameters
metrics_beam5.json — full test-set metrics (beam search k=5)

Usage

bash

git clone https://github.com/OmarGamal488/flickr-image-captioning.git
cd flickr-image-captioning
uv sync

Then in Python:

python

import pickle, torch
from huggingface_hub import hf_hub_download
from src.inference import load_attention_model, caption_image
from src.utils import get_device

repo_id = "OmarGamal48812/flickr-captioning"
ckpt_path = hf_hub_download(repo_id=repo_id, filename="attention_gru_glove.pth")
vocab_path = hf_hub_download(repo_id=repo_id, filename="vocab.pkl")

device = get_device()
with open(vocab_path, "rb") as f:
    vocab = pickle.load(f)

encoder, decoder, cfg = load_attention_model(ckpt_path, len(vocab), device)

caption, beams = caption_image(
    encoder, decoder, "your_image.jpg", vocab, device,
    method="beam", beam_width=5,
)
print(caption)
for b in beams[:3]:
    print(f"  {b.score:+.3f}  {b.caption}")

Limitations

Domain. Trained on Flickr8k + Flickr30k photos (mostly people, dogs, outdoor scenes). Performance degrades on cartoons, screenshots, and abstract imagery.
Safe-word bias. Only 8.8% of the 10,111-word vocabulary is used at inference — the decoder converges on template phrases like "a man in a white shirt is standing".
No object counting. The attention context vector collapses object count — the model often says "a dog" when the image shows two dogs.
Hallucinations. The decoder can insert objects not in the image when visual evidence is weak and the language-model prior takes over.
English only. Vocabulary and grammar are entirely from English Flickr captions.

Citation

If you use this checkpoint, please cite the three papers this work builds on:

bibtex

@inproceedings{xu2015show,
  title     = {Show, Attend and Tell: Neural Image Caption Generation with Visual Attention},
  author    = {Xu, Kelvin and Ba, Jimmy and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and
               Salakhutdinov, Ruslan and Zemel, Richard and Bengio, Yoshua},
  booktitle = {ICML},
  year      = {2015}
}

@article{bahdanau2014neural,
  title   = {Neural Machine Translation by Jointly Learning to Align and Translate},
  author  = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
  journal = {arXiv preprint arXiv:1409.0473},
  year    = {2014}
}

@inproceedings{selvaraju2017gradcam,
  title     = {Grad-{CAM}: Visual Explanations from Deep Networks via Gradient-based Localization},
  author    = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and
               Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle = {ICCV},
  year      = {2017}
}