yolo_finetuned_fruits

yolandadiaz23/yolo_finetuned_fruits

yolo_finetuned_fruits is a object detection model from yolandadiaz23. 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

1% of all-time downloads

All-time downloads

299

Public

Parameters

6.5M

777 MB 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: Mar 19, 2026
Updated: Mar 19, 2026
SHA: 8d5fb713

Base models

hustvl/yolos-tiny

Spaces

yolandadiaz23/yolo-racoon

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: 0.7751
Map: 0.6707
Map 50: 0.8905
Map 75: 0.7644
Map Small: -1.0
Map Medium: 0.3683
Map Large: 0.7213
Mar 1: 0.6304
Mar 10: 0.8217
Mar 100: 0.8391
Mar Small: -1.0
Mar Medium: 0.5
Mar Large: 0.8805
Map Raccoon: 0.6707
Mar 100 Raccoon: 0.8391

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_FUSED 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 Raccoon	Mar 100 Raccoon
No log	1.0	40	1.2756	0.031	0.0674	0.024	-1.0	0.016	0.0739	0.3	0.5152	0.7217	-1.0	0.46	0.7537	0.031	0.7217
No log	2.0	80	0.9756	0.2493	0.4052	0.2586	-1.0	0.1786	0.2915	0.4413	0.6891	0.7913	-1.0	0.44	0.8341	0.2493	0.7913
No log	3.0	120	0.9065	0.377	0.5753	0.4031	-1.0	0.2407	0.3998	0.4739	0.6783	0.8152	-1.0	0.42	0.8634	0.377	0.8152
No log	4.0	160	0.9019	0.4623	0.6709	0.5483	-1.0	0.3312	0.4905	0.5348	0.7587	0.7891	-1.0	0.5	0.8244	0.4623	0.7891
No log	5.0	200	0.8960	0.5412	0.7918	0.5917	-1.0	0.3884	0.5619	0.5739	0.7522	0.7957	-1.0	0.5	0.8317	0.5412	0.7957
No log	6.0	240	0.8825	0.5764	0.8242	0.6359	-1.0	0.3129	0.6122	0.5957	0.787	0.8217	-1.0	0.46	0.8659	0.5764	0.8217
No log	7.0	280	0.8669	0.6501	0.8942	0.7602	-1.0	0.3901	0.6881	0.6174	0.7826	0.8065	-1.0	0.48	0.8463	0.6501	0.8065
No log	8.0	320	0.8239	0.6512	0.8759	0.7918	-1.0	0.4126	0.6861	0.6522	0.787	0.8217	-1.0	0.5	0.861	0.6512	0.8217
No log	9.0	360	0.8016	0.6388	0.8556	0.7304	-1.0	0.3333	0.687	0.6239	0.7957	0.8174	-1.0	0.52	0.8537	0.6388	0.8174
No log	10.0	400	0.9311	0.6044	0.8975	0.6823	-1.0	0.3766	0.6339	0.5783	0.7826	0.8239	-1.0	0.52	0.861	0.6044	0.8239
No log	11.0	440	0.8713	0.6278	0.8823	0.7214	-1.0	0.4172	0.6581	0.5804	0.7935	0.813	-1.0	0.52	0.8488	0.6278	0.813
No log	12.0	480	0.8208	0.6674	0.8973	0.7697	-1.0	0.3661	0.7089	0.6261	0.7804	0.8261	-1.0	0.52	0.8634	0.6674	0.8261
0.7933	13.0	520	0.8489	0.6615	0.8949	0.7469	-1.0	0.3351	0.7101	0.6261	0.8	0.813	-1.0	0.5	0.8512	0.6615	0.813
0.7933	14.0	560	0.7815	0.6655	0.8891	0.7832	-1.0	0.3819	0.703	0.6217	0.7957	0.8283	-1.0	0.5	0.8683	0.6655	0.8283
0.7933	15.0	600	0.7896	0.6641	0.8963	0.7597	-1.0	0.3886	0.7024	0.637	0.8087	0.8261	-1.0	0.52	0.8634	0.6641	0.8261
0.7933	16.0	640	0.7731	0.6776	0.911	0.763	-1.0	0.3733	0.7151	0.637	0.8239	0.8391	-1.0	0.52	0.878	0.6776	0.8391
0.7933	17.0	680	0.7913	0.6896	0.9077	0.7854	-1.0	0.408	0.7238	0.663	0.8174	0.8391	-1.0	0.52	0.878	0.6896	0.8391
0.7933	18.0	720	0.7925	0.6738	0.908	0.7728	-1.0	0.3816	0.7139	0.6283	0.8174	0.8435	-1.0	0.52	0.8829	0.6738	0.8435
0.7933	19.0	760	0.7738	0.6818	0.8988	0.7732	-1.0	0.3899	0.7218	0.6348	0.8239	0.837	-1.0	0.54	0.8732	0.6818	0.837
0.7933	20.0	800	0.7956	0.6669	0.8968	0.7502	-1.0	0.3719	0.7041	0.637	0.8174	0.8326	-1.0	0.48	0.8756	0.6669	0.8326
0.7933	21.0	840	0.7787	0.6637	0.8964	0.7554	-1.0	0.3502	0.706	0.6196	0.8239	0.8413	-1.0	0.5	0.8829	0.6637	0.8413
0.7933	22.0	880	0.7779	0.6746	0.9078	0.7677	-1.0	0.3804	0.7104	0.6217	0.8283	0.8413	-1.0	0.5	0.8829	0.6746	0.8413
0.7933	23.0	920	0.7735	0.6655	0.8853	0.7315	-1.0	0.3884	0.7135	0.6435	0.8304	0.8457	-1.0	0.5	0.8878	0.6655	0.8457
0.7933	24.0	960	0.7760	0.6657	0.8775	0.7426	-1.0	0.3669	0.7223	0.6457	0.8283	0.8435	-1.0	0.5	0.8854	0.6657	0.8435
0.4159	25.0	1000	0.7626	0.6636	0.8835	0.7577	-1.0	0.3831	0.7103	0.6283	0.8239	0.837	-1.0	0.5	0.878	0.6636	0.837
0.4159	26.0	1040	0.7661	0.6627	0.8857	0.7546	-1.0	0.3935	0.7154	0.6283	0.8217	0.837	-1.0	0.5	0.878	0.6627	0.837
0.4159	27.0	1080	0.7813	0.6714	0.891	0.7643	-1.0	0.3734	0.722	0.6304	0.8217	0.8391	-1.0	0.5	0.8805	0.6714	0.8391
0.4159	28.0	1120	0.7769	0.6693	0.8897	0.7633	-1.0	0.3734	0.7209	0.6304	0.8217	0.8391	-1.0	0.5	0.8805	0.6693	0.8391
0.4159	29.0	1160	0.7768	0.671	0.8909	0.7647	-1.0	0.3734	0.7213	0.6304	0.8217	0.8391	-1.0	0.5	0.8805	0.671	0.8391
0.4159	30.0	1200	0.7751	0.6707	0.8905	0.7644	-1.0	0.3683	0.7213	0.6304	0.8217	0.8391	-1.0	0.5	0.8805	0.6707	0.8391

Framework versions

Transformers 4.57.6
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Task: Object Detection
Library: transformers
Type: yolos
License: apache-2.0
Created: Mar 19, 2026
Updated: Mar 19, 2026