State Farm Logo Detection
Detect State Farm logos in NBA broadcast footage. The base model produces 30 false positives and misses 18 logos across the test set. After RL fine-tuning, F1 reaches 1.0 with zero false positives and zero false negatives.
F1
| Method | RL |
| Steps | 139 |
| Training time | 1 hr 21 min |
| Cost | $19.57 |
See it in action
Switch between benchmark examples to compare the base model against the fine-tuned model on the same task.
Prompt
state farm logo
Base Moondream 3 Preview
mIoU 0.469
Fine-tuned Moondream 3 Preview
mIoU 0.920+0.451Perfection in 3 steps
What is fine-tuning?
Moondream starts as a general model trained on broad, public information. Fine-tuning makes it great at one specific task by teaching it the products, documents, categories, or internal information that matter to your business.
Who is this for?
This is for teams putting vision AI into production. If you already know the task and need the model to master that job, fine-tuning is how you get there. It is built for teams that need frontier performance at real-time speed.
See the code
Fine-tuning is just a small API loop: format your data, call `train_step`, and the model updates as you go.
import moondream as md
# Create fine-tune
ft = md.ft(
api_key="your-api-key",
name="state farm logo detection",
rank=8,
)
# Hidden boilerplate and data code
requests = (
(
example,
{
"skill": "detect",
"image": example["image"],
"object": "state farm logo?",
"num_rollouts": 4,
},
)
for example in training_data
)
for context, response in ft.rollout_stream(requests):
rewards = compute_rewards(context, response)
ft.train_step([{
"mode": "rl",
"request": response["request"],
"rollouts": response["rollouts"],
"rewards": rewards,
}])Frequently asked questions
Ready to take Moondream to production?
Need help? We'll build it for you.
We can help define the task, prepare the data, run training, validate results, and hand off a model your team can use.