What it does DeepLogo trains an SSD-Inception detector to spot 27 brand logos (Adidas to Yahoo) in images. It handles the full grunt work: cleaning dirty annotations from the Flickr Logos 27 dataset, converting to TFRecord format, and wiring up the TF1 Object Detection API’s training and evaluation scripts. The author also hosts a pre-trained checkpoint on Google Drive so you can skip training entirely.

The interesting bit The README is essentially a battle-scarred field manual for TF1’s Object Detection API. It documents the exact commit hash of tensorflow/models that works (5ba3c3f5), the symbolic-link hack to bridge directory structures, and the specific legacy/train.py script path — the kind of tribal knowledge that usually lives in forgotten forum threads.

Key highlights

• Fine-tunes SSD-Inception v2 pre-trained on COCO; no architecture novelty, just applied plumbing
• Includes preprocessing to strip invalid bounding boxes (empty sizes) from the Flickr Logos 27 annotations
• Provides a pre-trained model download; inference via logo_detection.py dumps results to --output_dir
• Evaluation setup includes the exact num_examples count (438) needed for the test set
• Author has since moved on to DeepLogo2 using Facebook’s DETR

Caveats

• Hard-locked to TensorFlow 1.x; explicitly broken on TF 2.0
• Requires cloning the full tensorflow/models repo and downgrading to a specific commit
• The “Detection Results” grid shows output samples but no quantitative metrics (mAP, precision, etc.)

Verdict Worth a look if you’re maintaining legacy TF1 detection pipelines or need a complete, minimal worked example of the Object Detection API end-to-end. Skip it if you want modern architecture or TF2/PyTorch; the author already did.