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 Per Class	Mar 100 Per Class
No log	1.0	28	2.5936	0.0318	0.0619	0.022	-1.0	0.0045	0.0413	0.0571	0.1286	0.2571	-1.0	0.1571	0.2905	-1.0	-1.0
No log	2.0	56	2.5445	0.0359	0.0639	0.0458	-1.0	0.0028	0.0453	0.0536	0.1607	0.2357	-1.0	0.1857	0.2524	-1.0	-1.0
No log	3.0	84	2.4228	0.0312	0.0724	0.0315	-1.0	0.0057	0.0394	0.0929	0.1714	0.2643	-1.0	0.1429	0.3048	-1.0	-1.0
No log	4.0	112	2.3942	0.0328	0.0907	0.0251	-1.0	0.0085	0.0415	0.0679	0.1821	0.2679	-1.0	0.1571	0.3048	-1.0	-1.0
No log	5.0	140	2.0300	0.0467	0.1679	0.0249	-1.0	0.0088	0.0601	0.1107	0.2429	0.3679	-1.0	0.1714	0.4333	-1.0	-1.0
No log	6.0	168	1.7810	0.0783	0.2271	0.0319	-1.0	0.0173	0.0998	0.1357	0.2714	0.4357	-1.0	0.2714	0.4905	-1.0	-1.0
No log	7.0	196	0.9484	0.3502	0.5473	0.4019	-1.0	0.1203	0.4418	0.3893	0.5786	0.7	-1.0	0.5	0.7667	-1.0	-1.0
No log	8.0	224	0.9857	0.3475	0.6586	0.3079	-1.0	0.2263	0.4302	0.3679	0.5821	0.6321	-1.0	0.5	0.6762	-1.0	-1.0
No log	9.0	252	0.9677	0.3873	0.6941	0.3508	-1.0	0.2413	0.4696	0.375	0.6143	0.6571	-1.0	0.4429	0.7286	-1.0	-1.0
No log	10.0	280	0.8982	0.4398	0.7283	0.4886	-1.0	0.3602	0.5114	0.3929	0.675	0.6964	-1.0	0.5714	0.7381	-1.0	-1.0
No log	11.0	308	0.9651	0.4521	0.7699	0.4456	-1.0	0.349	0.5323	0.3643	0.6643	0.7	-1.0	0.5571	0.7476	-1.0	-1.0
No log	12.0	336	0.8820	0.4423	0.7258	0.4994	-1.0	0.4573	0.501	0.3786	0.6786	0.6893	-1.0	0.6286	0.7095	-1.0	-1.0
No log	13.0	364	0.9836	0.3962	0.6924	0.3714	-1.0	0.2744	0.4651	0.4071	0.6429	0.65	-1.0	0.4857	0.7048	-1.0	-1.0
No log	14.0	392	0.8974	0.437	0.7174	0.4754	-1.0	0.4242	0.4956	0.3929	0.6786	0.6964	-1.0	0.5714	0.7381	-1.0	-1.0
No log	15.0	420	0.9634	0.4514	0.7789	0.352	-1.0	0.4134	0.5065	0.4107	0.6429	0.6679	-1.0	0.5571	0.7048	-1.0	-1.0
No log	16.0	448	0.9124	0.4381	0.7877	0.395	-1.0	0.4701	0.4867	0.4143	0.6464	0.6786	-1.0	0.6143	0.7	-1.0	-1.0
No log	17.0	476	0.8533	0.4909	0.8212	0.4254	-1.0	0.516	0.5298	0.4357	0.6929	0.7071	-1.0	0.6429	0.7286	-1.0	-1.0
0.9876	18.0	504	0.8571	0.5018	0.8166	0.4559	-1.0	0.501	0.5422	0.4536	0.6964	0.725	-1.0	0.6286	0.7571	-1.0	-1.0
0.9876	19.0	532	0.8877	0.5031	0.8409	0.4599	-1.0	0.5469	0.5389	0.425	0.6786	0.7036	-1.0	0.6571	0.719	-1.0	-1.0
0.9876	20.0	560	0.9169	0.478	0.8151	0.4376	-1.0	0.5086	0.5085	0.4214	0.6643	0.6786	-1.0	0.6	0.7048	-1.0	-1.0
0.9876	21.0	588	0.9110	0.4807	0.8345	0.3706	-1.0	0.516	0.5131	0.4	0.6607	0.6893	-1.0	0.6143	0.7143	-1.0	-1.0
0.9876	22.0	616	0.8786	0.5032	0.8384	0.435	-1.0	0.5497	0.5299	0.4321	0.6857	0.7143	-1.0	0.6857	0.7238	-1.0	-1.0
0.9876	23.0	644	0.8691	0.515	0.8463	0.4496	-1.0	0.5216	0.5517	0.4393	0.6893	0.7143	-1.0	0.6571	0.7333	-1.0	-1.0
0.9876	24.0	672	0.9108	0.4691	0.8359	0.365	-1.0	0.4832	0.5017	0.3893	0.6607	0.6929	-1.0	0.6143	0.719	-1.0	-1.0
0.9876	25.0	700	0.8910	0.5068	0.8419	0.4934	-1.0	0.4928	0.5425	0.4286	0.6893	0.7	-1.0	0.6	0.7333	-1.0	-1.0
0.9876	26.0	728	0.8788	0.5119	0.8484	0.4668	-1.0	0.4976	0.551	0.4357	0.6821	0.7036	-1.0	0.6143	0.7333	-1.0	-1.0
0.9876	27.0	756	0.8795	0.507	0.8502	0.4567	-1.0	0.4976	0.5441	0.4321	0.6857	0.7036	-1.0	0.6143	0.7333	-1.0	-1.0
0.9876	28.0	784	0.8807	0.5057	0.8503	0.4568	-1.0	0.4976	0.5427	0.4286	0.6893	0.7036	-1.0	0.6143	0.7333	-1.0	-1.0
0.9876	29.0	812	0.8821	0.5055	0.8508	0.4567	-1.0	0.4976	0.542	0.4286	0.6857	0.7036	-1.0	0.6143	0.7333	-1.0	-1.0
0.9876	30.0	840	0.8823	0.5024	0.8508	0.4567	-1.0	0.4976	0.5381	0.4286	0.6821	0.7036	-1.0	0.6143	0.7333	-1.0	-1.0

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

ahmando/yolo_finetuned_fruits

yolo_finetuned_fruits

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ahmando/
yolo_finetuned_fruits