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 Banana	Mar 100 Banana	Map Orange	Mar 100 Orange	Map Apple	Mar 100 Apple
No log	1.0	51	1.8714	0.0185	0.0565	0.0135	-1.0	0.0048	0.0231	0.0829	0.258	0.4361	-1.0	0.1833	0.4439	0.0172	0.575	0.0115	0.2957	0.027	0.4375
No log	2.0	102	1.5650	0.0681	0.1376	0.0497	-1.0	0.0383	0.0722	0.1851	0.3816	0.5832	-1.0	0.5667	0.5829	0.1018	0.7667	0.049	0.487	0.0534	0.4958
No log	3.0	153	1.2170	0.1247	0.2409	0.1309	-1.0	0.5139	0.1254	0.2853	0.5664	0.6775	-1.0	0.6833	0.679	0.0943	0.7833	0.1562	0.5826	0.1237	0.6667
No log	4.0	204	1.1502	0.176	0.3108	0.1643	-1.0	0.6616	0.1725	0.3002	0.5545	0.6675	-1.0	0.7833	0.6655	0.1299	0.7083	0.1235	0.5609	0.2746	0.7333
No log	5.0	255	0.9764	0.1611	0.2632	0.1633	-1.0	0.5489	0.1627	0.4061	0.6376	0.7559	-1.0	0.7167	0.7571	0.0982	0.7917	0.1559	0.7261	0.2293	0.75
No log	6.0	306	0.9906	0.2365	0.3988	0.2255	-1.0	0.5446	0.2397	0.3861	0.6407	0.7404	-1.0	0.7167	0.7403	0.1409	0.7833	0.2365	0.7087	0.3321	0.7292
No log	7.0	357	0.9680	0.3394	0.5742	0.3693	-1.0	0.4131	0.3394	0.3644	0.6524	0.7286	-1.0	0.7333	0.7309	0.2329	0.7667	0.3237	0.6609	0.4615	0.7583
No log	8.0	408	0.9377	0.4575	0.727	0.5243	-1.0	0.749	0.4487	0.4196	0.6898	0.728	-1.0	0.75	0.7305	0.334	0.7583	0.4491	0.713	0.5895	0.7125
No log	9.0	459	0.8636	0.5069	0.7174	0.5779	-1.0	0.649	0.502	0.4383	0.7167	0.7607	-1.0	0.6667	0.7675	0.4216	0.7583	0.5113	0.7696	0.5879	0.7542
1.1722	10.0	510	0.9071	0.489	0.7123	0.581	-1.0	0.6062	0.4837	0.4014	0.6803	0.7266	-1.0	0.6333	0.735	0.4341	0.725	0.4408	0.7174	0.5922	0.7375
1.1722	11.0	561	0.9232	0.549	0.8138	0.644	-1.0	0.6657	0.5445	0.4589	0.6952	0.7246	-1.0	0.7667	0.7253	0.5481	0.7833	0.5046	0.6696	0.5942	0.7208
1.1722	12.0	612	0.8283	0.5653	0.7854	0.6578	-1.0	0.698	0.5653	0.5091	0.7333	0.7826	-1.0	0.7667	0.7846	0.5022	0.8083	0.5185	0.7478	0.6752	0.7917
1.1722	13.0	663	0.8513	0.5606	0.7952	0.6923	-1.0	0.5998	0.5648	0.4913	0.7136	0.7557	-1.0	0.7	0.7601	0.5567	0.7667	0.5153	0.713	0.6097	0.7875
1.1722	14.0	714	0.8491	0.5922	0.8466	0.6543	-1.0	0.719	0.5924	0.4948	0.7205	0.7923	-1.0	0.8	0.7955	0.5943	0.8167	0.5218	0.7435	0.6604	0.8167
1.1722	15.0	765	0.8062	0.6224	0.8476	0.752	-1.0	0.7371	0.6231	0.4995	0.7418	0.7826	-1.0	0.8333	0.7831	0.6025	0.775	0.5775	0.7478	0.6873	0.825
1.1722	16.0	816	0.8432	0.5857	0.8189	0.7143	-1.0	0.6861	0.5884	0.4839	0.7321	0.787	-1.0	0.8167	0.788	0.5805	0.8	0.5387	0.7652	0.6379	0.7958
1.1722	17.0	867	0.8424	0.5918	0.8354	0.6386	-1.0	0.6583	0.5956	0.4896	0.7164	0.7687	-1.0	0.7333	0.774	0.6002	0.7667	0.5275	0.7435	0.6477	0.7958
1.1722	18.0	918	0.8354	0.5821	0.8358	0.6492	-1.0	0.6607	0.5871	0.5061	0.7236	0.7811	-1.0	0.7333	0.7871	0.5738	0.7917	0.5479	0.7391	0.6246	0.8125
1.1722	19.0	969	0.8060	0.5926	0.843	0.66	-1.0	0.6672	0.5979	0.4814	0.7237	0.7911	-1.0	0.7333	0.7967	0.5555	0.8167	0.5714	0.7609	0.651	0.7958
0.6614	20.0	1020	0.8114	0.6041	0.8394	0.69	-1.0	0.7438	0.6055	0.4937	0.7389	0.788	-1.0	0.8	0.7916	0.5721	0.8	0.5581	0.7391	0.6821	0.825
0.6614	21.0	1071	0.7965	0.6165	0.8495	0.7003	-1.0	0.708	0.6213	0.4786	0.7476	0.7995	-1.0	0.7667	0.807	0.5631	0.8	0.5985	0.7652	0.6879	0.8333
0.6614	22.0	1122	0.8093	0.6003	0.8431	0.6807	-1.0	0.7245	0.6032	0.4771	0.7475	0.7995	-1.0	0.7667	0.8066	0.555	0.8083	0.5715	0.7652	0.6745	0.825
0.6614	23.0	1173	0.7949	0.6095	0.8432	0.6921	-1.0	0.7299	0.6126	0.491	0.7348	0.8011	-1.0	0.7667	0.8083	0.5832	0.8	0.5728	0.7826	0.6726	0.8208
0.6614	24.0	1224	0.8203	0.6027	0.8451	0.683	-1.0	0.7157	0.6034	0.4786	0.7418	0.801	-1.0	0.7667	0.8081	0.5664	0.8083	0.5677	0.7696	0.674	0.825
0.6614	25.0	1275	0.8308	0.6057	0.8391	0.6659	-1.0	0.7157	0.6087	0.4841	0.7375	0.8009	-1.0	0.7667	0.8078	0.5741	0.8167	0.5687	0.7652	0.6743	0.8208
0.6614	26.0	1326	0.8213	0.6102	0.8524	0.6994	-1.0	0.7298	0.6117	0.4938	0.7335	0.7982	-1.0	0.7833	0.8041	0.5753	0.7917	0.5715	0.7696	0.6837	0.8333
0.6614	27.0	1377	0.8115	0.6073	0.8491	0.7042	-1.0	0.728	0.609	0.4911	0.7335	0.7967	-1.0	0.7833	0.8023	0.5741	0.8	0.5758	0.7609	0.6719	0.8292
0.6614	28.0	1428	0.8048	0.6111	0.8503	0.707	-1.0	0.7225	0.6152	0.4842	0.7393	0.7969	-1.0	0.7667	0.8045	0.5686	0.7833	0.5865	0.7739	0.6783	0.8333
0.6614	29.0	1479	0.8054	0.6118	0.8489	0.71	-1.0	0.7225	0.6165	0.4842	0.7393	0.7969	-1.0	0.7667	0.8045	0.5686	0.7833	0.588	0.7739	0.6787	0.8333
0.5101	30.0	1530	0.8053	0.6121	0.8489	0.71	-1.0	0.7225	0.6168	0.4842	0.7393	0.7969	-1.0	0.7667	0.8045	0.5688	0.7833	0.588	0.7739	0.6794	0.8333

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

lucasaf04/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lucasaf04/
yolo_finetuned_fruits