yolo_finetuned_fruits

diribes/yolo_finetuned_fruits

yolo_finetuned_fruits is a object detection model from diribes. Use it when you need objects located in an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

3% of all-time downloads

All-time downloads

233

Public

Parameters

6.5M

3.2 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors25.9 MB · 100%

At a glance

Task: Object Detection
Library: transformers
License: apache-2.0
Model type: yolos
Access: Public
Created: Apr 28, 2025
Updated: Apr 30, 2025
SHA: 0768be5b

Base models

hustvl/yolos-tiny

Model card

From the Hugging Face model README

yolo_finetuned_fruits

This model is a fine-tuned version of hustvl/yolos-tiny on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7791
Map: 0.4714
Map 50: 0.6956
Map 75: 0.5409
Map Small: -1.0
Map Medium: 0.4873
Map Large: 0.5087
Mar 1: 0.4392
Mar 10: 0.71
Mar 100: 0.7539
Mar Small: -1.0
Mar Medium: 0.6438
Mar Large: 0.7737
Map Banana: 0.3709
Mar 100 Banana: 0.7275
Map Orange: 0.4969
Mar 100 Orange: 0.7429
Map Apple: 0.5463
Mar 100 Apple: 0.7914

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Map	Map 50	Map 75	Map Small	Map Medium	Map Large	Mar 1	Mar 10	Mar 100	Mar Small	Mar Medium	Mar Large	Map Banana	Mar 100 Banana	Map Orange	Mar 100 Orange	Map Apple	Mar 100 Apple
No log	1.0	60	1.9961	0.0048	0.0162	0.0025	-1.0	0.0199	0.0051	0.0369	0.1339	0.2356	-1.0	0.1223	0.2534	0.0042	0.245	0.0063	0.2619	0.0038	0.2
No log	2.0	120	1.7598	0.0221	0.0672	0.0129	-1.0	0.0577	0.0216	0.0639	0.1861	0.3615	-1.0	0.1723	0.3772	0.0439	0.5525	0.0137	0.1548	0.0088	0.3771
No log	3.0	180	1.5885	0.0344	0.0984	0.0195	-1.0	0.04	0.0394	0.1276	0.2906	0.4912	-1.0	0.2277	0.5277	0.0393	0.5725	0.0242	0.3381	0.0398	0.5629
No log	4.0	240	1.7055	0.0432	0.1236	0.0231	-1.0	0.086	0.0453	0.1399	0.2912	0.4533	-1.0	0.1991	0.493	0.0514	0.48	0.0468	0.3143	0.0313	0.5657
No log	5.0	300	1.5373	0.0441	0.0939	0.0325	-1.0	0.0775	0.049	0.1611	0.3362	0.4907	-1.0	0.117	0.5434	0.0504	0.595	0.0585	0.3429	0.0233	0.5343
No log	6.0	360	1.3281	0.078	0.1589	0.0696	-1.0	0.1315	0.0958	0.2488	0.4444	0.6168	-1.0	0.4062	0.6491	0.0527	0.6375	0.1145	0.6214	0.0667	0.5914
No log	7.0	420	1.1093	0.084	0.1721	0.0795	-1.0	0.2091	0.0909	0.2741	0.5155	0.6735	-1.0	0.5036	0.6996	0.0753	0.6875	0.0937	0.65	0.0829	0.6829
No log	8.0	480	1.0723	0.1198	0.2091	0.126	-1.0	0.3302	0.1316	0.3113	0.5349	0.6929	-1.0	0.5598	0.7189	0.0885	0.635	0.1448	0.681	0.1259	0.7629
1.4412	9.0	540	0.9907	0.1246	0.2195	0.1319	-1.0	0.1973	0.1443	0.3778	0.5932	0.7191	-1.0	0.5759	0.7445	0.1133	0.6925	0.1306	0.6762	0.1298	0.7886
1.4412	10.0	600	0.9855	0.1517	0.2615	0.1465	-1.0	0.2562	0.1671	0.35	0.5819	0.6753	-1.0	0.4375	0.7129	0.1153	0.685	0.1784	0.6381	0.1613	0.7029
1.4412	11.0	660	0.9734	0.1793	0.2934	0.1965	-1.0	0.2641	0.1978	0.3564	0.6029	0.6922	-1.0	0.4839	0.7249	0.1385	0.6975	0.1879	0.6476	0.2114	0.7314
1.4412	12.0	720	1.0457	0.2177	0.3468	0.2265	-1.0	0.2242	0.2489	0.3676	0.6224	0.6648	-1.0	0.4259	0.704	0.158	0.66	0.255	0.6286	0.2399	0.7057
1.4412	13.0	780	0.8756	0.2393	0.3799	0.2619	-1.0	0.3545	0.2646	0.4163	0.6819	0.7369	-1.0	0.6054	0.7577	0.1926	0.745	0.2709	0.7143	0.2546	0.7514
1.4412	14.0	840	0.9067	0.2987	0.4602	0.3327	-1.0	0.4857	0.305	0.3873	0.6723	0.7256	-1.0	0.6661	0.7371	0.2039	0.7025	0.3302	0.7429	0.362	0.7314
1.4412	15.0	900	0.9761	0.2658	0.4491	0.2969	-1.0	0.3493	0.2926	0.3656	0.6225	0.7037	-1.0	0.5911	0.7232	0.2068	0.685	0.3045	0.6976	0.286	0.7286
1.4412	16.0	960	0.9318	0.2791	0.4399	0.3218	-1.0	0.3934	0.3035	0.3802	0.6542	0.7144	-1.0	0.6054	0.733	0.2017	0.7	0.306	0.6976	0.3295	0.7457
0.8426	17.0	1020	0.8593	0.3076	0.4558	0.3454	-1.0	0.42	0.3289	0.4046	0.678	0.7545	-1.0	0.6589	0.7731	0.2056	0.7125	0.3782	0.7452	0.3389	0.8057
0.8426	18.0	1080	0.8634	0.3121	0.5056	0.3313	-1.0	0.4074	0.347	0.4025	0.6684	0.7438	-1.0	0.6393	0.7646	0.2542	0.6925	0.3362	0.7476	0.3459	0.7914
0.8426	19.0	1140	0.8064	0.3787	0.5725	0.3908	-1.0	0.422	0.4135	0.4251	0.7022	0.7504	-1.0	0.6313	0.774	0.2907	0.695	0.4079	0.7476	0.4376	0.8086
0.8426	20.0	1200	0.7830	0.4137	0.6134	0.4445	-1.0	0.4597	0.446	0.4294	0.7059	0.7565	-1.0	0.6259	0.7801	0.3242	0.7225	0.4305	0.75	0.4864	0.7971
0.8426	21.0	1260	0.7738	0.4224	0.6321	0.468	-1.0	0.4326	0.4516	0.4314	0.7087	0.7619	-1.0	0.6232	0.7866	0.3299	0.7325	0.4396	0.7619	0.4977	0.7914
0.8426	22.0	1320	0.8010	0.429	0.6543	0.4765	-1.0	0.4505	0.4649	0.4238	0.7088	0.7403	-1.0	0.6027	0.7643	0.3371	0.7175	0.4546	0.7262	0.4953	0.7771
0.8426	23.0	1380	0.7663	0.4368	0.6546	0.473	-1.0	0.4744	0.4744	0.4363	0.7014	0.7561	-1.0	0.6571	0.7754	0.3623	0.7125	0.4574	0.7643	0.4906	0.7914
0.8426	24.0	1440	0.7869	0.4652	0.704	0.5348	-1.0	0.4769	0.4996	0.4319	0.7171	0.75	-1.0	0.6295	0.7716	0.3588	0.7225	0.5102	0.7476	0.5265	0.78
0.6721	25.0	1500	0.7694	0.466	0.6751	0.5348	-1.0	0.4933	0.5055	0.4348	0.7088	0.752	-1.0	0.6509	0.7704	0.3672	0.725	0.4856	0.731	0.5452	0.8
0.6721	26.0	1560	0.7674	0.4673	0.6805	0.5337	-1.0	0.4829	0.5079	0.4362	0.7093	0.7527	-1.0	0.6375	0.7737	0.3688	0.7225	0.4927	0.7357	0.5405	0.8
0.6721	27.0	1620	0.7790	0.4742	0.7018	0.5431	-1.0	0.486	0.5139	0.4414	0.7103	0.7557	-1.0	0.6438	0.7759	0.3763	0.7275	0.5016	0.7452	0.5448	0.7943
0.6721	28.0	1680	0.7775	0.4726	0.6975	0.5412	-1.0	0.4855	0.5109	0.4383	0.7101	0.7522	-1.0	0.6366	0.7728	0.3748	0.7275	0.4959	0.7405	0.5472	0.7886
0.6721	29.0	1740	0.7791	0.4715	0.6957	0.5409	-1.0	0.4873	0.5089	0.4392	0.7109	0.7539	-1.0	0.6438	0.7737	0.3715	0.7275	0.4969	0.7429	0.5462	0.7914
0.6721	30.0	1800	0.7791	0.4714	0.6956	0.5409	-1.0	0.4873	0.5087	0.4392	0.71	0.7539	-1.0	0.6438	0.7737	0.3709	0.7275	0.4969	0.7429	0.5463	0.7914

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

Task: Object Detection
Library: transformers
Type: yolos
License: apache-2.0
Created: Apr 28, 2025
Updated: Apr 30, 2025