This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 100 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2
Open-weight model · Object detection
202311536_furniture_finetuning
by Syc0413 syc0413/202311536_furniture_finetuning
202311536_furniture_finetuning is an open-weight model for object detection from Syc0413, released under Apache License 2.0. It has 42M parameters and a 1,024-token context. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
Parameters42M
Context1,024
Weights166.5 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve 202311536_furniture_finetuning (42M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
202311536_furniture_finetuning on every accelerator the SAVRN Index prices, at every precision
Model Card
By Syc0413, published under apache-2.0, revision cf828b9e5ca6.
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 100 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2
Read Syc0413's full model card
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
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: 1e-05 - train_batch_size: 8 - 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: linear - num_epochs: 100 - mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
Configuration
- Architecture
- DetrForObjectDetection
- Context length (tokens)
- 1,024
- Layers
- 6
- Model type
- detr
Identity and Version
- Repository
- syc0413/202311536_furniture_finetuning
- Publisher
- Syc0413
- Task
- Object detection
- Modality
- Image
- Library
- transformers
- Parameters
- 42M parameters
- Languages
- Not stated by the source
- Revision
- cf828b9e5ca644ff8e7015903e799084429f8dc4
- First published
- 2026-10-09
- Last updated
- 2026-10-10
Files and Weights
6 files, 166.5 MB in total. The weights are 2 files totalling 166.5 MB in bin, safetensors.
Weights2 files · 166.5 MB
Configuration2 files · 1.9 KB
Documentation1 file · 1.3 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 166.5 MB | 7c4b9afbe10b |
| training_args.bin | Weights | 5.8 KB | f014ec385177 |
| config.json | Configuration | 1.4 KB | — |
| preprocessor_config.json | Configuration | 455 B | — |
| README.md | Documentation | 1.3 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 166.5 MB
Download from Syc0413
Released by Syc0413 through its official repository on Hugging Face. Read the license.
Built From
- Derived from facebook/detr-resnet-50
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 166.5 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About 202311536_furniture_finetuning
How much GPU memory does 202311536_furniture_finetuning need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (42M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run 202311536_furniture_finetuning on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use 202311536_furniture_finetuning commercially?
Yes. 202311536_furniture_finetuning is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is 202311536_furniture_finetuning's context length?
1,024 tokens, from the maximum position embeddings in its published configuration.
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