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name: tensorflow-yolov3 description: "Guides legacy TensorFlow 1.x YOLOv3 workflows for data preparation, checkpoint conversion, frozen-graph inference, training, and Pascal VOC mAP evaluation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

tensorflow-yolov3

Use this repo skill when a task mentions YunYang1994/tensorflow-yolov3, TensorFlow 1.x YOLOv3, core.yolov3.YOLOV3, yolov3_coco.pb, COCO checkpoint conversion, VOC annotation lists, train.py, evaluate.py, or the bundled mAP workflow.

This is a legacy script-style repository, not an installable Python package. Most source scripts assume they are run from a YOLOv3 working directory where relative paths like ./data/classes/coco.names, ./data/anchors/basline_anchors.txt, ./checkpoint/..., and ./mAP/... exist.

Start here

1. Read repository provenance before deciding whether this skill is current for a checkout.

2. Read environment notes before installing dependencies or debugging TensorFlow import failures.

3. Run the bundled environment checker from the runtime skill root when a user has a local working copy:

   python scripts/check_environment.py --repo-root <repo-root>
   

4. Route the user request to the narrowest sub-skill below.

Sub-skill routes

• data-preparation: class files, anchors, annotation text rows, Pascal VOC conversion, and dataset-input validation before training or evaluation.
• conversion: release COCO checkpoint remapping, --train_from_coco, freezing yolov3_coco.pb, output tensor names, and known Darknet conversion script bugs.
• inference: frozen .pb image/video/camera inference, tensor contract checks, preprocessing, postprocessing, NMS, and empty-detection troubleshooting.
• training: core/config.py training fields, Dataset, two-stage training, logs/checkpoints, COCO initialization, and long-run/GPU/data risk checks.
• evaluation: evaluate.py, generated mAP/ground-truth and mAP/predicted files, Pascal VOC AP/mAP formats, and isolated mAP fixture checks.

Shared references

• architecture: Darknet-53/YOLOv3 graph structure, config defaults, verified tensor shapes, and helper APIs shared across sub-skills.
• environment: dependency/version guidance for TensorFlow 1.x, Python, protobuf, OpenCV/Pillow, and headless systems.
• troubleshooting: cross-cutting install, relative-path, missing-weight, missing-data, and legacy GPU issues.

Minimal import and graph sanity check

For a local working copy, use a TensorFlow 1.x-capable environment and run from the working copy root:

python - <<'PY'
import tensorflow as tf
from core.yolov3 import YOLOV3

tf.reset_default_graph()
input_data = tf.placeholder(dtype=tf.float32, shape=(1, 416, 416, 3), name='input_data')
model = YOLOV3(input_data, trainable=False)
print(model.pred_sbbox.shape.as_list())  # [1, 52, 52, 3, 85] for COCO classes
print(model.pred_mbbox.shape.as_list())  # [1, 26, 26, 3, 85]
print(model.pred_lbbox.shape.as_list())  # [1, 13, 13, 3, 85]
PY

If that import fails before the graph is built, read environment and troubleshooting first. A common cause is importing core.utils outside a working directory where ./data/classes/coco.names exists.

Do not overclaim verification

The checkout used for this skill did not include full checkpoint shards, a frozen PB, or complete VOC image datasets. The generated guidance was verified with source inspection, TensorFlow 1.x CPU graph construction, and safe helper/script checks. Full training, real inference, and checkpoint conversion still require user-supplied model/data artifacts and a compatible TensorFlow 1.x runtime.